Riding the bicycle to work, walking up the stairs to the apartment, taking a book from the shelf, and playing the violin are all examples of motor activities, which are signified by the planning of future goal states, the coordination of different limbs and various whole-body postures, and the regulation of muscle forces during the dynamic control of the different movements. The skillful coordination of such movement actions is one of the major challenges in mastering the various tasks of our daily life. To do so, people do not only passively take in the information from the environment with their senses when they interact with objects or with other people but also plan their movements in advance to change the environment in a purposeful way. Most of the time, people’s movements are therefore voluntary, goal-directed, and intentional, relying mainly on higher-level perceptual–cognitive control processes. This is markedly different from motor reflexes, which are hardwired, involuntary, and unintentional, relying on lower-level sensorimotor control processes and providing the basic building blocks of human behavior (e.g., stepping reflex, grasping reflex). This entry focuses on the performance of complex movements that can be readily observed in various sport disciplines and settings. Of chief interest are three aspects: (1) conceptualizing movements within different taxonomies, (2) practicing movements within different schedules, and (3) mastering movements at different levels of expertise.
Conceptualizing Movements Within Different Taxonomies
There are at least three ways to differentiate the various kinds of movements into different taxonomies. The first is to consider the number of different movement elements, components, or actions that need to be generated and executed. In this context, three different categories can be identified: discrete movements, serial movements, and continuous movements. Discrete movements are characterized by a well-defined beginning and endpoint. The motor action is usually executed within a short time window. Examples of discrete movement skills are kicking a soccer ball, throwing punches, or dancing a pirouette. Serial movements are of longer duration, as they consist of two or more discrete motor actions, which are executed in an ordered sequence of events. A gymnastic routine, high-jumping, or the turnaround in swimming are examples of serial movement skills. Continuous movements have no recognizable beginning and end, as the different elements are repeated in a way that the movement’s final phase merges into the new initial phase, starting the whole series of motor action all over again. Examples of continuous movement skills are running, swimming, and cycling.
The second way to classify movement skills into different categories is to look at the relative importance of perceptual, motor, and cognitive elements and components for task performance. Perceptual skills rely mainly on the processing of visual, auditory, and/or tactile information, whereas the execution of corresponding motor action is of less importance. For a catcher in baseball, the demands on the motor elements (and on decision making [DM]) of the catching action are (considerably) simple compared to the demands on visual anticipation of the fastball pitch. Motor skills depend primarily on the quality of movement performance. A weight lifter, for example, must be able to maximize his lift-up and push-out actions, in order to realize a heavy weight. Cognitive skills require complex DM and the use of strategies. The strong safety in American football must recognize different playing patterns of the attacking team and come up with quick decisions for his defensive maneuvers. When looking at these three previous examples, however, it should be noted that task performance for each of these different types of movement skills does not solely rely on a single element or component. Rather, the focus is on the movement element or component, which is mainly “responsible” for successful task performance.
The third way of skill categorization is to address the context-specific environmental demands during task performance. On one end of the continuum, these may be always stable and predictable, whereas on the other end, task demands may be constantly changing in a most unpredictable way. Accordingly, movements are either closed skills or open skill, or they are in between these two poles somewhere on the continuum. The extent to which changes in the environment can be predicted may be further affected by time constraints and/or the interaction with teammates and opponents. Playing darts is an example of a closed skill, where the task demands are always similar and the environmental context does not change, while the discrete movement can be executed without much time pressure and no physical interaction with the opponent. The same is true for high-jumping (indoors), but the serial movement has to be adapted to changes in the task context, as the bar is raised higher. Backcountry skiing is more complex, because serial and continuous movements have to be adapted to the (natural) environment of the skiing resort, making this a slightly more open skill. Still more complex and open is the Olympic discipline of freestyle skiing, because there is high time pressure, as the skiers race against one opponent (although without coming into physical contact). Playing rugby is an example of an open skill, where performance must be adapted to varying task demands and a constantly changing environment, under high time pressure and against a number of different opponents.
Practicing Movements Within Different Schedules
Movements become skills when they are practiced deliberately. K. Anders Ericsson, Ralf Krampe, and Clemens Tesch-Römer defined deliberate practice as “activities that have been specially designed to improve the current level of performance” (p. 368). Thereby, distinct changes of performance are the direct result of the activities experienced during the training session. Most all of the time, such activities (i.e., physical practice) lead to effects of performance improvement, which can be readily observed in an increase of some critical variables (e.g., score points, reaction time [RT], movement time [MT], performance errors). But under specific circumstances, physical practice can also produce effects of performance decrement. Take the implementation of a new training method or drill as an example. After this new method or drill has been introduced into training, athletes may not be able to perform a certain skill with the efficiency as before. Such changes of performance, however, may only be of short duration, when the movement is not practiced over a longer amount of time. More permanent changes of performance are thought to reflect effects of motor learning, which results from extended amounts of physical (and mental) practice. Characteristic effects associated with motor learning are the higher consistency of performance and better performance results, the optimization of movement economy and the (quasi) automatic execution of skills, as well as the ability to flexibly adapt any movement to fast changes in the environment. Also, with extended practice, the execution of even the most complex movement skills will demand less attentional resources, which can be used to direct attention to other aspects in the environment. Whether or not an athlete has actually learned from practice can only be assessed with a retention test (i.e., testing the level of performance after a period without practice) and/or a transfer test (i.e., testing the level of performance in a new task or under a new task context).
Deliberate practice can be scheduled in many different ways. However, the most important variable of motor learning—with all other factors being equal—is practice. That is, only a large amount of deliberate practice will improve the level of performance in the long term. Many studies therefore focused on how to schedule physical training and to distribute the amount of practice for a particular skill. Consider the following example: A basketball player would like to improve her performance for the jump shot. She can go ahead and perform 100 jump shots, without a break and always from the same spot on the court. Such a training schedule is referred to as massed practice. Alternatively, she may perform 10 shots in a row, take a short break, and then continue with the next 10 shots, take a break, continue for 10 shots, and so on, for a total amount of (again) 100 jump shots from the same spot. This is called distributed practice. As research shows, athletes will learn a certain skill better when the activity is distributed over several blocks of practice, as seen in better retention and transfer test results. Some of the benefits of distributed practice schedules may be explained with the recovery of perceptual, cognitive, and motor functions during practice breaks.
In this basketball example, the player was practicing the jump shots always from the same spot and thus shooting the ball to the basket over the same distance in all of the practice trials. This is referred to as constant practice. A way to alter this practice schedule would be to introduce some variability into the task conditions. To this end, the player could move up closer to the basket on some trials and move farther away from it on other trials, shooting either from shorter or longer distances. This is an example of variable practice. Hence, the amount of variability may be another factor to consider when scheduling practice session. Much research shows that practicing movements under constant conditions increases the level of performance for a short period of time, whereas performance improvements are more permanent under variable conditions. If variable practice is more beneficial than constant practice, then the question that needs to be answered in this regard is how to schedule the variable conditions. Assume that the basketball player would now like to practice the 100 jump shots from four different distances (e.g., 2, 3, 4, and 5 m). She could perform 25 shots in a row from each distance, before moving on for another 25 consecutive shots from the second distance, and so on. Alternatively, she could take only a single shot from one distance and then move to another distance in a random sequence, without attempting to shoot from any distance twice in a row. One schedule is organized as blocked practice and the other schedule as random practice. Again, more variability within a particular schedule seems to benefit motor learning and improve movement skills in the long term. This has been explained either with a deeper conceptual processing of the movement during practice or with ongoing forgetting and reconstruction processes, which benefit the retention and transfer of the movement.
Mastering Movements at Different Levels of Expertise
The deliberate practice approach suggests that extensive amounts of domain-specific practice are necessary to reach an expert level of performance. It has been suggested that athletes need at least 10,000 hours of deliberate practice or more than 10 years of training within a particular field, such as music, typing, chess, and sports. Most interestingly, it seems that everyone can become an expert performer, for as long as he or she will put in the effort of such prolonged and extensive practice for at least 4 to 5 hours a day over the number of years. If large amounts of practice are dedicated to train a single skill, the level of performance may become exceptionally high. For example, the basketball player referred to before most likely spends much time to practice his or her shots from the foul line. As a result, the likelihood of scoring from the foul line will be much higher than what would be expected when only considering the foul line shooting distance relative to the success rate from locations nearby. Such an exceptional performance in sports relies on so-called especial skills, which are an important factor of domain-specific expertise.
Before an athlete becomes an expert for any movement or sports skill, however, he or she moves through different stages of proficiency during the learning process. For the acquisition of new movement skills, Paul Fitts and Michael Posner proposed a three-stage learning model: In the first stage, the cognitive stage, the novice performer tries to get an idea of what to do and how to solve a particular movement problem. He or she learns the basic sequence of events constituting a particular movement. Movement execution is aided by verbal cues. At this early point in time, movements are performed slowly, inaccurately, and with the generation of too much force, resulting in more or less stiff-looking motor actions. Much attention is drawn to the organization and execution of the movement pattern. In the second stage, the associative stage, the advanced performer intentionally varies different task components and associates these variations with success or failure. Therefore, the quality of feedback that the learner receives plays a major role on skill acquisition during this phase. Overall, the level of performance is high as long as the task context remains stable and the movement can be carried out without much interference by an opponent and time pressure. Movement execution relies less on conscious (verbal) control and more on automatic processes. In the third stage, the autonomous stage, the expert performer produces his or her movements virtually without spending much thought on it. Movement execution is realized quickly, with high precision, and almost effortlessly. Skilled performance is achieved by automatic processes, allowing the athlete to focus on other important aspects during competition.
Motor learning is associated with (relatively) permanent changes of the procedural memory system. Based on extended deliberate practice, movement experts can rely on distinct memory structures during DM in sports, in which so-called basic action concepts (BACs) provide the representational basis for the voluntary control of complex movements. In high-level experts, these representational frameworks are organized in a distinctive hierarchical treelike structure, are remarkably similar between individuals, and are well matched with the functional and biomechanical demands of the task. In comparison, movement representations in novices are organized less hierarchically, are more variable between persons, and are less matched with the task’s functional and biomechanical demands. Thus, the quality of any movement performance may strongly depend upon the quality of movement representations in the procedural memory system.
Expertise in a number of game sports, however, may also be reflected by the degree of the athlete’s individual bilateral competence, which signifies his or her ability to perform particular skills well on both sides of the body. The basketball player who is able to handle the ball with equal proficiency with the dominant and the nondominant hand has an advantage, because he or she can readily adopt the execution of skills to fast changes of play (e.g., while switching from the dominant to the nondominant hand), enabling him or her to flexibly adjust movements to new situations. In contrast, the athlete who is not able to handle the ball with equal proficiency on both sides may not be capable to adjust his or her play sufficiently to new situations and is (therefore) constrained to perform a particular skill with the dominant hand, even if the situation requires use of the nondominant hand. Thus, the generation of optimal solutions for different game situations in basketball (and other game sports) highly depends on the ability to perform specific movement skills with either side of the body, without much decrement in performance when using the nondominant hand. A lack of bilateral competence may hinder the progression to higher levels of competitive play in a number of game sports, harming the professional development of athletes.
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