Gallahue & Ozmun, 1998; Haywood, 1993; Payne & Isaacs, 1999; Rose,. 1997; Schmidt & Lee, 1999). The earliest empirical studies to use dynamic. Address for ...
Int. J. Sport Psycho!., 1999; 30:507-530
Understanding Development and Learning of Motor Coordination in Sport: The Contribution of Dynamic Systems Theory DANIELA CORBETTA"', and BEATRIX VEREI]KEN 1"''
.
·.~Department of Health, Kinesiology, and Leisure Studies and Department of Psychological Sci-
ences, Purdue University, 1\7est Lafayette, USA of Psyd;ology, Norwegian University of Science and Technology, Trondheim, Norway
,.,..~Department
To date, our understanding of learning and development offundamental motor skills in sport still relies heavtly on stage-lzke approaches. The aim of this article is to shozu how dynamic systems theory can provide compelling concepts and methods to go beyond stage approaches and study the transitions from one stage to another. W'e begin by introducing the major d_vnamic systems concepts and methods that we deem essential to tbe stud_v of motor learning and development. Then z~.-·e illustrate through two exemplczr studies how the application of these concepts and methods have shed new light on old developmental questions and the learning of complex coordination. Finally, we discuss how dynamic systems theory can be used to study and teach fundamental motor skills in children. In the conclusion, we invite practitioners and scientists to empirically test and verify our claims. KEY WoRDS:
Development, Dynamic systems theory, Learning, Motor skills.
Dynamic systems theory made its appearance in the field of motor studies about two decades ago (Kelso, Holt, Kugler, & Turvey, 1980; Kugler, Kelso, & Turvey, 1980; Turvey, 1977). Since then, the popularity of this theoretical framework has increased dramatically to the point that, nowadays, most textbooks in motor control, motor learning, and motor development introduce the major tenets and characteristics of this approach (Gabbard, 1996; Gallahue & Ozmun, 1998; Haywood, 1993; Payne & Isaacs, 1999; Rose, 1997; Schmidt & Lee, 1999). The earliest empirical studies to use dynamic
Address for correspondence: Daniela Corbetta, Department of HKLS, Purdue University, 1362 Lambert. West Lafayette. IN 47907 (USA)
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systems theory focused primarily on skilled coordination patterns in tasks involving either the repetitive flexion and extension of the index-fingers (Hak. en, Kelso, & Bunz, 1985; Schaner & Kelso, 1988), the bimanual manipulation of hand-held pendulums (Kugler & Turvey, 1987; Schmidt, Beek, Treffner, & Turvey, 1991; Turvey, Rosenblum, Kugler, & Schmidt, 1986), or the leg swinging motion between individuals (Schmidt, Carella, & Turvey, 1990). These groundbreaking modeling studies were instrumental in demonstrating the implications of dynamic systems theory to understand movement behavior and coordination. In particular, they revealed cQmplex, self-organizing principles that are governing movement coordination and control in biological systems, thereby providing important advances to our conceptualization of movement behavior. Despite its present popularity, the general use and application of dynamic systems theory continues to be dominated by skilled adult performance and the coordination of limited parts of the body. When it comes to less skilled behavior (as in learning and development) and whole-body coordination (as in most sports), examples are few and far between. Although there is an increase in the application of dynamic systems theory in developmental and learning studies at both ends of the developmental spectrum, such as in infants (Clark, Truly, & Phillips, 1993; Thelen, 1994; Vereijken & Thelen, 1997; Whitall & Getchell, 1995) and adult populations (Schaner, Zanone, & Kelso, 1992; Vereijken, 1991; Zanone & Kelso, 1992), it is still rarely used to study the development of fundamental motor skills in childhood. Similarly, while there are some examples of dynamic systems studies involving wholebody coordination (Jeka, Kelso, & Kiemel, 1993; Kelso & Jeka, 1992; Vereijken, Whiting, & Beek, 1992), this theoretical framework still has to penetrate the broader area of sport sciences. The question is thus how one proceeds from dynamic systems studies on skilled, simple movements to applying dynamic systems theory to the learning and development of complex movement formation in sport. This paper aim·s to provide an understanding of this issue. Although development and learning differ importantly in terms of the conditions under which they take place - development occurring against the backdrop of a more rapidly changing body and a smaller repertoire of existing skills - they are largely similar with respect to the operating processes of change. As the focus in this paper is to address the general processes of change, the terms development and learning will both be used to indicate the acquisition of new skills, under any condition and at all ages. In contrast to more technical tutorials in dynamic systems theory (] eka & Kelso, 1989; Kelso, Ding, & Schaner, 1993), we ;vill keep our language as 508
free of jargon, modeling, and mathematics as possible and. introduce only those concepts we deem to be fundamental to the study of development and learning in sport. We will begin by introducing the key concepts and methods of dynamic systems theory to demonstrate why this theory provides such a compelling approach to study the development and learning of motor coordination. Subsequently, we will elaborate upon two exemplar studies that successfully applied this framework to investigate complex motor development and learning, respectively. We borrowed these examples from infants' studies, because the early period of development in particular deals with the emergence and formation of new behaviors and theref6re provides a unique window into the processes of change. Finally, in the last section, we will discuss the implications of dynamic systems theory for the acquisition of fundamental motor skills in children. Our goal is to illustrate how concepts in dynamic systems theory can help both researchers and educators to go beyond the description of developmental stages and understand the psychological and physical mechanisms underlying the dynamic formation of movement coordination as a function of developmental and learning time.
Development, Learning, and Dynamic Systems Theory
When we think about development and learning, we think about change, transformation, progress, or improvement over time. Developmental and behavioral sciences have been very proficient at describing change by capturing typical forms of behavior, developmental stages, or levels of performance that succeed one another across the life span. Well-known examples of orderly sequences of universal stages can be found in Piaget's developmental stage theory (1936/1952), in Gesell's studies of developmental growth patterns and norms (Gesell & Thompson, 1938; Gesell, Thompson, & Amatruda, 1934), and in McGraw's (1945) theory of neuromuscular maturation. Stage descriptions and successive levels of performance have dominated the field of movement and sport sciences as well. Most descriptive studies on fundamental motor skills in childhood - like running, jumping, throwing, hopping, and so forth- are based on stage-like and age-typical descriptions of successive patterns of behavior (see for example, Halverson & Williams, 1985; Roberton, 1977; Seefeldt & Haubenstricker, 1982; Wickstrom, 1983; Wild, 1938). Similarly, the motor learning literature has characterized changes in motor coordination, movement planning, and skill acquisition as a succession of stages (see for example, Adams, 1971; Anderson, 1982; Bernstein, 1967; Schmidt, 1991; Snoddy, 1926). 509
Stage-like approaches have been instrumental at depicting the unfolding of the typical order or sequence in learning and development, but they have been unfortunately weak at providing insights into the processes affecting change, development, and learning. Defining stages or typical forms of behavior is an important starting point for studying change. But the necessary next step is to capture the transitions as they take place in the organism, and identify the pertinent dimensions of the behavior and/or the environment that induce the transformation to the next stage. One of the most fundamental reasons why we believe that dynamic systems theory is appropriate and can contribute to the study of development and learning, is that it is an explicit theory of change, devoted to capture, study, and understand structural and behavioral transitions occurring in, amongst others, living systems. Below, we summarize the main concepts and methods that dynamic systems theory uses to define and capture both the behavioral stages and the mechanisms that drive the system through these stages. Dynamic systems theory employs the term stability to describe existing states or identifiable forms of behavior, and the terms instability and transitions to describe alterations and changes from a given state to another state as a function of time. In general, instabilities can emerge from environmental and/ or internal modifications that are affecting the behavioral patterns of the movement system. Therefore, unstable patterns are generally considered as predictors of change (Kelso, Ding & Schaner, 1993). They bring flexibility to the system and allow the emergence and formation of new modes of functioning. The behavioral patterns before and after a transition, on the other hand, are comparatively stable and are labeled behavioral states, or preferred modes of coordination. These stable patterns tend to be relatively resilient to change. So far, developmental and learning studies have primarily focused on the behavioral states (or stages) that are marking relatively stable periods in the behavioral patterning of a system in evolution. As we will illustrate in the next section, dynamic systems theory shifted the focus of traditional, stageoriented studies toward understanding behavioral instabilities and capturing the critical elements that elicit the emergence of transitional periods, thereby providing a window into the processes of change. Both the stability and the instability of behavioral patterns can be the result of self-organizing processes that do not necessarily require infinite regress to prescriptions- such as general motor programs -or executive agents like neural maturation. Rather, order can emerge in any system that consists of many components as a function of energetic interactions between internal and external conditions, without a priori specification of the emerging order. Two concepts are important in this context: degrees of freedom of the system 510
and constraints on possible configurations of the system. By definition, a complex system consists of many components and many different possible orderings between these components. These are referred to as degrees of freedom. Our anatomy provides for many more degrees of freedom than are strictly needed for the performance of a task, as is indicated by the multiple ways in which a task can be carried out, but a confluence of constraints reduces the number of options available in a particular situation. Constraints on action are defined as any decrease in the number of orderings that are possible at a given time, regardless of the mechanism of reduction. Constraints come in many guises and several attempts have been·made to categorize them (Pattee, 1972; Warren, 1990). The most popular categorization of constraints on action stems from Newell (1986) who divided them according to whether they originated in the organism, the specific task, or the ambient environment (see also van der Kamp, Vereijken, & Savelsbergh, 1996). When studying the processes of skill acquisition, motor development, and motor learning, it is useful to make a distinction between intrinsic dynamics and task dynamics (Kelso, 1995). Whenever we learn a new skill, we do this against the backdrop of already existing capacities and functional properties of the system (Kelso, 1995; Thelen & Smith, 1994). Intrinsic dynamics are defined as· the spontaneous coordination tendencies or preferred modes of coordination that exist in the movement system at the start of a learning process. In other words, intrinsic dynamics capture the initial state of an organism when faced with a new learning or developmental task, reflecting the history of the system and prior experiences that contribute to form the existing behavioral repertoire. Task dynamics, on the other hand, are specific influences that stem from the task, intention, or relevant information in the environment that are acting on these intrinsic dynamics. \"X'hen learning a new task or developing a new skill, the intrinsic dynamics are not necessarily in agreement with the dynamics of the task that is to be learned, leading to so-called competition (Schaner & Kelso, 1988). In that case, extended practice can modify the intrinsic dynamics to accommodate the new task and the desired behavioral pattern. If, on the other hand, the task dynamics happen to correspond to the existing intrinsic dynamics, learning the new pattern will be easier (Zanone & Kelso, 1992). The occurrence of cooperation between intrinsic and task dynamics may well underlie the poorly understood phenomenon of learning transfer between two different skills. That is to say, compatibility between task and intrinsic dynamics would predict the occurrence of learning transfer and facilitate change. Incompatibility, on the contrary, necessitates more time and effort from the learner to modify the original behavior. Identifying existing preferred modes of coordination before engaging in a learning or sport program is a critical step for the coach or 511
teacher and will help design a more effective practice program that is calibrated to the skill level of the learner (Payne & Isaacs, 1999). We contend that this is where stage-like approaches provide useful information for the teacher or coach, as at any given point in the history of the system, stages reflect specific intrinsic dynamics underlying the observed modes of coordination. The existence of intrinsic dynamics that reflect the prior history of the organism points to the importance of previous experiences in processes of learning and development. As previous experiences can be expected to differ between different subjects, the large inter-individual variability that we often find in learning and developmental studies take on a new meaning. They are not necessarily noise but can also reflect specific, individual solutions and pathways to solve specific, individual problems (see for example, Adolph, Vereijken, & Denny, 1998; Corbetta & Thelen, 1995, 1996; Thelen, Corbetta, Kamm, Spencer, Schneider, & Zernicke, 1993). A successful method to capture self-organizing processes and changes in the complex web of interactions that take place between the multiple components of a system and its environment has been to simultaneously lay out the developmental course of these ·different behavioral components as a function of time. This approach was termed a relational, multilevel approach (Thelen, 1986b), and has allowed the observation of changes in patterning by looking at how the different components of a behavior interact, self-assemble, and cooperate with each other. It is precisely within this complex process of interactions and cooperation between components of the behavior, the environmental conditions, and task constraints, that dynamic change takes place. In the following section, we will provide an explicit illustration of how this multilevel approach can be implemented in the study of behavior. We will show that the dynamic transitions resulting from the changing interactions between behavioral components directly inform us about the ongoing developmental coordination processes. Two other common methods that provided useful insights in the dynamic organization and formation of behavior have been to either provoke sudden changes in the environment causing a momentary perturbation in the system (Schaner, 1995), or to induce a progressive change in the environment following a steady continuum until the system is constrained to make a shift (or a bifurcation) to another mode of functioning (Kelso, 1995). Both methods were successful at identifying stability and predicting change in a system. But most importantly, these methods were effective at demonstrating that without specific initial intent or prescription, the system was able to spontaneously develop preferred modes of coordination to fit new environmental constraints. These studies were instrumental to set the stage for understand512
ing the dynamic processes involved in learning new skills. We will illustrate in the following section how the use of perturbation and bifurcation methods provided new insights in understanding development and learning in movement coordination. The above concepts of intrinsic dynamics, self-organization, (in) stability, and transitions have been used in several developmental and learning studies that aimed at understanding more complex behaviors such as the development of reaching or stepping in infancy (Thelen, Corbetta, Kamm, Spencer et al., 1993; Vereijken & Thelen, 1997), locomotion in toddlers (Clark, Truly & Phillips, 1993; Whitall & Getchell, 1995), clapping in children (Fitzpatrick, Schmidt, & Lockman, 1996), or learning slalom ski-like movements (Vereijken, 1991) and juggling (Beek & van Santvoord, 1992) in adults. Some of these studies used modeling tools to capture the laws governing movement coordination but, as indicated before, our goal is not to emphasize the modeling aspects of these studies, but rather to point out how the application of dynamic systems' concepts and methods as described above were effective at unraveling specific processes of behavioral formation and change. In the next section, we will present two infant studies that looked at the dynamics of developing and learning both discrete and cyclical complex motor skills. We chose infant studies because they provide a unique window into the processes of change. Infants have a comparatively limited prior history and develop a wide range of new fundamental skills in a comparatively short time span. These two characteristics make infants ideal candidates to examine how novel forms of coordination emerge from the interplay between intrinsic dynamics and environmental constraints. Each example will start with a brief introduction of the skill of interest and the traditional view on its acquisition. We will then illustrate how the application of dynamic systems theory and the concepts introduced above led to new insights, paying particular attention to how a relational, multilevel approach and induced changes in the environment contributed to uncover processes in behavioral transformation in both developmental and learning time.
Applying Dynamic Systems Theory to Motor Development and Learning THE TRANSITION TO REACHING IN INFANCY:
A RELATIONAL, MULTILEVEL
APPROACH
Reaching is one of the first behaviors that allows infants to act directly upon their environment (Gibson, 1988}, making its development around 4 513
months of age an important milestone in the infant's life. For that reason, understanding how reaching emerges and how it subsequent! y develops into an adapted and flexible skill has been a dominant theme in developmental research. So far, several studies have reported consistent changes in reaching performance throughout the first year. At first, infants' reaching attempts are laborious with indirect and tortuous trajectories to the target (Berthier, 1996; Thelen, Corbetta, Kamm et al., 1993; von Hofsten, 1991). With practice, however, infants become better at directing their hand to the target location, leading to straighter and more effective reaches. In previous research, this progression in reaching·performance was largely attributed to changes in the guidance of vision (Piaget, 1936/1952). Terms such as visually-elicited and visually-guided reaching were used to characterize different stages of reaching development (Bushnell, 1985; von Hofsten, 1979; von Hofsten & Fazel-Zandy, 1984; White, Castle, & Held, 1964). In the past few years, however, the critical role of vision in the development of reaching has been down played. A series of studies from Clifton and collaborators (Clifton, Muir, Ashmead, & Clarkson, 1993; Clifton, Rochat, Robin, & Berthier, 1994; Perris & Clifton, 1988) demonstrated that when infants were provided with auditory information, they could successfully reach in the dark without seeing either their hand or the target. If vision is not critical in eliciting and refining infants reaching behaviors, how do infants initially control their reach? And how do they progress from the production of early jerky and tortuous reaching trajectories to performing smoother and straighter trajectories only a few months later? To shed new light onto these developmental questions, Thelen and collaborators (Thelen, Corbetta, Kamm et al., 1993; Thelen, Corbetta, & Spencer, 1996) used a relational, multilevel approach to specifically address the developmental aspects of trajectory formation in early reaching. To track developmental transitions and periods of stability in the development of infant reaching, they followed four infants longitudinally and at close time intervals throughout their first year of life. At each session, they collected and described multiple components of the developing behavior. First, they captured the changing intrinsic dynamics underlying reaching movements by recording extended time series of spontaneous arm movements performed before and after the reach. In addition, to provide insights into the coordination processes reflecting the evolution and developmental transitions in reaching trajectory, the authors laid out multiple components of the behavior studied as a function of developmental and real time. In particular, they studied hpw goal (attaining the target), timing (positional and joint angle time series of both arms), and load (time series of 514
the arms' intersegmental dynamics or kinetics), as defined by Schaner (1995), interacted with each other and self-organized through developmental time. Although changes in other dimensions of the developing system were tracked as well, such as transitions in interlimb coordination (Corbetta & Thelen, 1996), changes in muscle patterns (Spencer & Thelen, 1999), and progression in postural control (Vereijken, Spencer, Diedrich, & Thelen, 1999), we limit ourselves here to the dimensions that directly informed us on the coordination processes specific to trajectory formation. The aim of this study was not to describe change in each of these dimensions separately, but rather to pay attention to how these different levels interacted with each other at different points in development." As we aimed at capturing changes as a function of developmental time, no specific variations were introduced in the environment, and infants were always tested the same way throughout the entire year. This study revealed a series of new_ features about how early coordination processes in reaching emerge and change. First, the analysis of the spontaneous, non-reaching behaviors displayed by these infants revealed individual and changing intrinsic dynamics throughout the year. Two infants were very active before the onset of reaching but slowed down later. The other two infants were more quiet and contemplative before reach onset but became more active after reach onset (Thelen, Corbetta, Kamm et al., 1993; Thelen, Corbetta & Spencer 1996). It was found that these individual and changing levels of ongoing, spontaneous upper-arm activity exerted dramatic influence on trajectory formation in early reaching. In particular, infants had very different arm control problems to solve during their first reaching attempts which depended on their activity level. The two active infants who carved out their reaches from a background of fast, spontaneous movements, initiated their reaches with high movement speed, produced many directional changes in the hand trajectory, and generally ended with swiping motions at the target. The relational, multilevel analysis of the time series of the reach - goal, timing, and load -indicated what these infants were trying to achieve. The portions of the trajectory that corresponded to fast speed were always associated with high motion-dependent torques that tended to drag the arm away from its intended trajectory to the target. Interestingly, these reactive torques were always followed and counteracted by active muscle torques. The active muscle torques revealed that these infants were able to produce rapid on-line control of their arm to correct for trajectory deviations and bring their arm to the target (Corbetta & Thelen, 1995; Thelen, Corbetta, Kamm et al., 1993 ). In the weeks following reach onset, these active infants learned to better calibrate and control 515
the perturbing effects of motion-dependent torques by slowing down both their spontaneous and their reaching movements, which resulted in more direct trajectories to the target. The two less active infants produced straighter arm trajectories to the targets at reach onset. Because their movements were generally slow and of. ten initiated from a steady arm position, they did not generate high motiondependent torques that would have disrupted the trajectory. The challenge for these infants was to learn how to generate enough active forces to lift their arms against gravity (Corbetta & Thelen, 1995; Thelen, Corbetta, Kamm et al., 1993). Later in the year these infants passed through a period where they became more active, and this change in activity tremendously affected their patterns of reaching. Their early, fairly straight reaches deteriorated and became highly discontinuous suggesting that these infants, just like the two active infants, had to learn how to control the reactive forces generated by their now fast movements (Thelen, Corbetta & Spencer, 1996). Interestingly, infants' first reaching attempts were produced while the infants continuously and intensely looked at the target, therefore providing no evidence that the visual system was guiding the hand during the reach. Although the reaches may have been visually elicited, vision was not directly part of the ongoing arm control process. The process of bringing the hand to the target was instead related to the specific and complex control of the interactions between forces, speed, and direction in the mechanically-linked arm segments that was taking place during the act of reaching. Subsequent progress in the control of reaching trajectory was achieved by calibrating the forces intrinsic to the movement as a function of the specified goal.
TREADMILL STEPPING IN INFANCY: INFLUENCE OF INTRINSIC AND TASK DYNAMICS ON LEARNING
In the second example, we illustrate the application of dynamic systems theory to infant motor learning under enhanced training conditions. For over half a century, motor development was largely considered under the control of maturation. As a result, infants were believed to develop along a fixed sequence of increasingly complex motor milestones. Because maturation was considered as the factor driving change, enhanced training and enriched or impoverished environmental conditions were not believed to alter the basic quality of this orderly progression. In the seventies, two lines of evidence challenged this view, suggesting ipstead that even the classical motor mile516
stones could benefit from experience. First of all, cross-cultural studies revealed that different ways of handling infants significantly influenced the rate of motor development. Cultures where infants were encouraged by their parents to sit, stand, and walk developed these skills more precociously than infants from cultures where these skills received no particular emphasis (Super, 1976). Secondly, a now classic learning experiment by Zelazo, Zelazo, and Kolb (1972) showed that enhanced practice facilitated newborn stepping and prevented the usual disappearance of this response around 2 months old, this disappearance being until then considered as the product of maturation. Similarly, infants that were given daily sitting exercises for seven weeks were able to sit longer than infants that recei\led no such practice (Zelazo, Zelazo, Cohen, & Zelazo, 1993). The outcomes of these studies, however, were limited to quantitative effects of practice on development and were not able to demonstrate the specificity of the learning process on development, providing thus insufficient evidence to discard a maturational explanation. In order to give a more powerful illustration of learning in development and emphasize adaptive processes as well, we set out to demonstrate the plasticity of a developing neuromotor system by inducing qualitatively different changes in performance. The task we chose was cyclical treadmill stepping in pre-locomotor infants. When supported over a moving surface, even one-month-old infants will make stepping movements. The development of this early stepping pattern has been well documented in earlier treadmill studies (Thelen, 1986a; Thelen & Ulrich, 1991), making this pattern an ideal candidate for experimental manipulation. Initially, infant stepping on a treadmill is highly unstable. Steps are infrequent and irregular, and can be made with one leg only or both legs simultaneously in a variety of different patterns. Only sporadically do the two legs step in the more mature alternating fashion. Between three and four months of age, infants begin to step increasingly in an alternating mode, but variability in the pattern is still predominant. By the age of seven months, alternation has become the dominant mode and is performed in a regular, stable pattern, particularly on high treadmill speeds. It is in fact so stable that even severe perturbation -like a split-belt treadmill that drives one leg twice as fast as the other - does not disrupt the alternating pattern (Thelen, Ulrich, & Niles, 1987). In a recent experiment (Vereijken & Thelen, 1997), we tested how practice could drive unstable stepping patterns into different behavioral forms. For one month, three-month-olds were given daily practice in one of three conditions: practice on a slow-moving treadmill, practice on a fast-moving treadmill, or practice on a stationary treadmill. An additional group of 7517
month-olds with preferred fast stepping patterns was trained on a slowspeed treadmill, and a control group of 3-month-olds received no practice. To assess whether the intrinsic dynamics would change throughout the month of training, we monitored stepping performance in the experimental groups twice a week by a progressive change in the environment, in this case scaling the speed of the treadmill. The control group received this scalar probe only twice, once at the start of the experiment and again one month later. The questions we were able to address with this design included whether daily training on the treadmill would affect the quantity and quality of stepping, whether this effect would depend upon initial pattern stability, and whether individual intrinsic dynamics would i~teract with the task dynamics. Results indicated that stepping frequency in 3-month-old infants improved faster with practice on a moving treadmill than with no practice or practice on a stationary treadmill (Vereijken & Thelen, 1997). This increase was inversely related to initial performance: the more unstable initial stepping was, the more effective enhanced training was. Furthermore, enhanced training on a moving treadmill facilitated the transition from the initial mixture of different stepping patterns typical of 3-month-olds to a more predominant alternating stepping pattern. Practice on a stationary treadmill, on the other hand, strengthened the parallel stepping pattern, while without daily practice, the control infants maintained their initial mixture of steps across the month of the experiment. These training effects were heavily influenced by intrinsic dynamics, in that the preferred stepping patterns that existed at the start of the experiment often remained visible throughout the training period. This suggests that intrinsic dynamics continuously interacted with the learned pattern. Finally, we observed an interaction between the condition of training and training effects. Compatibility between initial performance and training condition led to a strengthening of initially preferred patterns, whereas competition between initial performance and training condition led to more diffuse training effects that generalized to other speeds than the training speed as well. In summary, the dynamic systems approach taken in this study demonstrated that motor learning in infancy is an adaptive process that can be modulated as a function of varying environmental conditions. It showed that the developing neuromotor system is indeed amenable to training, but mostly so when initial performance is unstable. More importantly, this study illustrated that infants' initial performance and preferences, i.e., their intrinsic dynamics, interact with the specifics of training, i.e., task dynamics, to produce differential effects. These findings allow for a new understanding of the nature of practice effects and the conditions for learning transfer.
Implications of Dynamic Systems Theory for the Study of Children's Acquisition of Fundamental Motor Skills The two examples presented above illustrated two aspects that we deem fundamental to the study of learning and development from a dynamic systems theory. Firstly, the behavioral solution of a new task does not need to be known in advance by the learner. Rather, the solution can emerge in a process of gradual discovery as complex interactions between the components of the behavior and the task take place. The second important aspect that was illustrated above was the fact that practice or exposure to certain environmental conditions can drive, facilitate, or alter the formation of specific movement patterns differently depending on the initial state of the system. These two aspects contrast sharply with most current attempts to teach fundamental motor skills in children. First, many elementary school programs tend to stress the behavioral solution of the motor skill through observational learning, i.e., the behavioral outcome is first demonstrated and then the student is asked to reproduce it. Second, learning is often assumed to result from the continuous repetition of the expected form of behavior regardless of the level of skill or previous experience of the learner. We propose a different approach and suggest different practices for teaching fundamental motor skills in children that rely primarily on two claims. First, the teaching of a new motor skill should not put exclusive emphasis on imitating or reproducing the final outcome, but also stress successive changes in movement coordination that may gradually drive the system towards the outcome. Second, training should not emphasize repetition as the major means to enhance performance, but rather provide a wide range of situations that will induce change in coordination, modify the intrinsic dynamics of the system, and provide a new set of experiences that will lead to the discovery of the final outcome. In this last section, we will first review the scarce research that attempted to evaluate the impact of previous teaching techniques on children's sport performance. We will point out where the problems are and offer a rationale as to why these techniques did not provide effective learning. We will then illustrate how concepts from a dynamic systems theory can be implemented to teach coordination and stimulate in particular the transitions from one level of performance to another. Our goal will be to explain that it is possible to use tasks that modify the initial coordination patterns of the learner and progressively drive the system toward a more advanced form of behavior, without relying necessarily on demonstration or repetition of the outcome.
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A LESSON FROM THE PAST The study of fundamental motor skills in childhood has been a topic of research for a long time (see, for example, the early work of Gutteridge, 1939; Wellman, 1937; Wild, 1938), but it did not become a prolific area of study until the late 1970s or early 1980s, when information processing theory was at its apotheosis (see for example, Branta, Haubenstricker, & Seefeldt, 1984; Halverson, Roberton, & Langendorfer, 1982; Halverson & Williams, 1985; Roberton, 1977; Seefeldt & Haubenstricker, 1982; Wickstrom, 1983). At that time, developmental stage approaches still heavily dominated the field of motor development. As a consequence, our current knowledge about the development and learning of fundamental motor skills in childhood is significantly marked by the respective influence of these two approaches. Developmental stage approaches yielded rich descriptions of orderly sequences through which children develop their throwing, catching, jumping, or hopping abilities. Textbooks, for example, either emphasize initial, elementary, and mature stages in the developmental patterning of these skills (Gallahue & Ozmun, 1998), or review the different approaches (i.e., component versus total body approach) used to describe sequential stages in the development of these behaviors (Gabbard, 1996; Payne & Isaacs, 1999). These are detailed and useful descriptions of the developmental progression of these skills that have direct implications for physical education, especially when it comes to identifying the stage of performance a child is in when joining a motor development program. But for the teacher involved in a motor development program or a coach concerned with the training of fundamental sport activities, identifying these stages is not enough. The bigger challenge lies in determining how to promote better skill performance and remedy inappropriate motor coordination patterns. In contrast to stage approaches, information processing theory did provide teachers and coaches with tools that could be readily implemented in their motor development programs. Central to the information processing theory is the idea that the learner is equipped with sophisticated cognitive capacities that are used to specify the goal, identify the cues pertinent to the goal, and make appropriate decisions to program the proper response. As part of this cognitive process, the learner is also equipped with a mechanism to compare the actual performance with the intended goal, either continuously while the movement is performed (closed-loop control), or at the end of the movement by comparing the end result with the intended outcome (open-loop control). In this model, the outcome of the skill has to be pre-defined in order to provide students with a reference to compare their perfor520
mance to. Hence, attempts to teach fundamental motor skills in children have been relying heavily on observational learning. That is, children are asked to watch a model and reproduce it, with the assumption that if they cannot reproduce the movement pattern accurately at first, they will progressively reach the goal by repeated comparisons of their performance to the outcome perceived earlier. As a consequence, previous teaching attempts have emphasized both how to adopt better postures and how to produce better movement sequences (Payne & Isaacs, 1999). These teaching strategies, however, have led to mixed results. For example, Halverson and collaborato~s (Halverson & Roberton, 1979; Halverson, Roberton, Safrit, & Roberts, 1977), who attempted to evaluate the impact of these teaching techniques on performance, used intensive guided practice in overarm throwing with kindergarten students in order to improve ball-throwing velocities in children. They found that instruction and guidance significantly improved the technique, that is, children developed more advanced forms of whole-body coordination to throw, but this change in technique did not affect performance and ballthrowing velocity. Providing verbal cues only resulted in both better technique and better performance of throwing (Fronske, Blakemore, & Abendroth-Smith, 1997) than the use of intense guided practice. Teaching children the solution by demonstrating the behavioral outcome or providing explicit cues such as guidance is very compelling, especially when we observe young performers and realize that many important aspects of the skill are missing. For example, children's performance often lacks a preparatory phase, it is often brought about by part of the body, like using only the arm for throwing in early developmental stages, and often lacks power or overall body adjustments. On the basis of these characteristics, one might suggest that movement sequencing and whole body coordination are missing in the early stages of development of these fundamental skills, leading to teaching strategies that emphasize how to use and sequence all body parts appropriately by demonstrating the fully coordinated movement. We contend that teaching more advanced forms of coordination by demonstrating the desired movement outcome may not necessarily teach actual coordination, especially if the observers are young children. As pointed out by Thomas, Michael, and Gallagher (1994), for example, throwing performance cannot improve if timing and coordination between arm action, forward stepping, and trunk rotation are not organized correctly. The act of throwing, just like catching, jumping, and other behavioral skills, results from the complex organization of the different elements that compose the 521
behavior. These components need to cooperate in a timely manner for successful coordination, performance, and power production. From our perspective, observational learning may not emphasize how these critical coordination components of the behavior must cooperate. Reproduction tasks put the emphasis on the postural components of the behavior, and posture may well be the only dimension that children are able to pay attention to when observing the model. Young children in particular may not be capable of extracting from the model other, potentially more important sources of information, such as timing or proper movement sequencing. Nor are they capable of understanding how these dimensions will affect force production. In addition, observati
