Ecological task analysis utilizing intrinsic measures in research and practice

ELSEXIER

Human Movement

Science 15 (1996) 285-314

Ecological task analysis Utilizing intrinsic measures in research and practice Allen W. Burton a3*, Walter E. Davis b ADivision of Kinesiology. UnirersiQ qf Minnesota,1900 Unirersi~ Ac,enue SE.,

Minneapolis,

MN

55455-0155. USA h Kent State Uni~*ersi@, Kent. USA

Abstract Ecological task analysis (ETA) originally was proposed by Davis and Burton in 1991 to facilitate the linking between basic and applied research and theory and application in the area of adapted physical education. There, four steps in an applied model were laid out based upon theory and research. In the present paper, additional empirical and theoretical work, including social referencing studies, which supports the key concepts of ETA are included, targeting a broader audience of persons in a variety of movement therapy and physical activity instruction professions. Following this expanded overview, the concept of using intrinsic measures to assess movement performance is addressed in detail, including applications of dimensional analysis, allometric procedures, and Froude numbers. These performer-scaled measures are offered as an important way to establish direct links between task goals and the constraints of the performer and the environment, one of the four guiding concepts of ecological task analysis. PsylNFO Keywords:

classification: Ecological

2330

task analysis;

Movement

assessment:

Movement

education;

Movement

therapy

1. Introduction Ecological task analysis (ETA), originally presented by Davis and Burton in 199 1, is perhaps the most detailed and comprehensive model which applies dynamical systems and related theories to the assessment and intervention of

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movement development. Dynamical systems is a term which broadly refers to the stability and change - or dynamics - of various physical and biological systems. In the context of human movement, ‘dynamical systems’ refers more specifically to the stability and change, and the nonlinearity of movement form as a function of the interaction between performer attributes, environmental context, and the intended task goal. A dynamical systems approach to studying human movement was described in detail by Kugler et al. (1980a, Kugler et al. (19821, but the foundation for this endeavor was established earlier by Turvey (19771, Turvey et al. (19781, and others. Numerous other papers followed, including several published in which the authors applied dynamical systems to assessment and intervention strategies for persons with movement deficits (e.g., Wagenaar, 1990; Wagenaar and Van Emmerik, 1994). Some were targeted for physical therapists (Fetters, 1991; Giuliani, 1991; Heriza, 1991; Kamm et al., 19901, some for occupational therapists (Dunn et al., 1994; Mathiowetz and Haugen, 19941, and yet others, namely ecological task analysis, for adapted physical educators (Davis and Burton, 1991). However, the applications of ecological task analysis clearly may extend beyond the field of adapted physical education. In recent papers, Balan and Davis (1993) have discussed the relevance of ecological task analysis inclusive for physical education, Burton and Davis ( 1992) have explained how ecological task analysis may be used to optimize the movement performance of persons with physical impairments, and Davis and Van Emmerik (1995a, Davis and Van Emmerik (1995b) have described how ecological task analysis may lead to a better understanding of motor development of persons with mental retardation. Accordingly, the purpose of this paper is to demonstrate the utility of ecological task analysis for any movement therapy or instruction context by elaborating the theoretical and empirical support for its four basic steps or procedures. But, before the rationale for the four steps are addressed, an overview of ecological task analysis will be provided by first contrasting it with traditional task analysis approaches, and then reviewing its foundational concepts. For more in-depth information on the theoretical and philosophical foundations of ecological task analysis and related research questions and strategies, the papers by Davis and Van Emmerik (1995a, Davis and Van Emmerik ( 1995b) should be consulted. 2. Overview of ecological task analysis Task analysis has been used in a variety of fields to decompose specific activities - such as performing an overhand throw, assembling a toy, operating a

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motor vehicle, or solving a geometry problem - into fundamental components to pinpoint sources of performance difficulties or to facilitate instruction or remediation. As indicated by the term itself, task analysis has traditionally focused only on the task and has not accounted for variations in the environment in which the task is performed or variations in performer attributes. Ecological task analysis, on the other hand, was designed to gain insight into the dynamics of movement behavior by examining the interacting constraints (both limitations and enablements) of performer, environment, and task. Indeed, this more ecological or global type of task analysis is necessary because a task, regardless of the domain of behavior, must be specified in terms of all categories of constraints, and the performer must discover, accommodate, and exploit these constraints in a manner which optimizes energy expenditure relative to the task goal (e.g., Vereijken et al., 1992b). Further, traditional task analyses are descriptive and prescriptive. In other words, they are used to (a) describe how specific tasks are successfully performed, usually determined by examining aggregate data; and (b) identify steps or components which may be added or modified to improve performance. Thus, in traditional task analyses, the task becomes confused with the solution such that they become inseparable. However, ecological task analysis is neither descriptive nor prescriptive because no assumption is made that there is a single best solution for a particular task. Conversely, ecological task analysis is based on the premise that are many possible solutions to a particular task, and these solutions are determined by the unique interaction of performer and environmental constraints with the goal or intent of the action. But, if ecological task analysis is neither descriptive nor prescriptive, what does it have to offer professionals who are interested in movement skill assessment and intervention? The separation of the movement solution from the task allows for any variation in movement from a standard or modal pattern to be, not a problem to be corrected, but a window into the dynamics of a person-action system. Observations of the degree of success in completing a task (movement product) and the stability and change in movement form (movement process) as performer and environmental constraints are systematically manipulated provides at least two important pieces of information. First, the specific contexts are identified in which a person can always accomplish the task, sometimes accomplish the task, and never accomplish the task. Second, the performer or environmental variables which elicit change in movement form may indicate what performer-environment systems or subsystems may be limiting the person to certain movement forms which in turn may limit movement products. Thus, the challenge in ecological task analysis, borrowing the words of

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Beek and Bingham (1991), is “to develop a taxonomy by measuring the qualitative properties that emerge in the context of specific tasks and to discover, by perturbation and interference, the significance of specific properties for the assembly and control of particular actions” (p. 40). The foundations of ecological task analysis were derived from work which can be placed under the umbrella of a dynamical systems approach to the study of action: Gibson’s ecological approach to perception, which encompasses the concept of affordances (Gibson, 1977, Gibson, 1979); Reed’s theory of action (Reed, 1982); Kugler et al.‘s reconceptualizations of coordination and control (Kugler et al., 1980a, Kugler et al., 1980b, Kugler et al., 1982; Kugler and Turvey, 1987); and Newell’s model of constraints (Newell, 1986). The guiding concepts of ecological task analysis which were distilled from this body of literature were, as stated by Davis and Burton (1991, p. 161): (a) actions are relations, not parts; (b) tasks should be categorized by function and intention, not mechanism; (c) invariant features of a task and variations within a task may be defined in terms of essential and nonessential variables, respectively; and (d) direct links should be established between the constraints of the task goal, the performer, and the environment. Each of these concepts were discussed in two previous papers (Burton and Davis, 1992; Davis and Burton, 1991). In this paper, only the last foundational concept will be addressed in the context of intrinsic measures of movement performance, following the explication of the procedures of ecological task analysis. In particular we provide empirical evidence that suggests approaches to intrinsic measurement which are feasible for use in clinical settings.

3. Four steps of ecological task analysis Ecological task analysis is comprised of four steps: (a> establishing task goals by structuring the physical and social environments; (b) allowing choices of movement solutions; (c) manipulating performer, environmental, or task variables; and (d) providing instruction (Davis and Burton, 1991). In this section, the theoretical and empirical underpinnings of each step will be described, including modifications and additions to the original model presented by Davis and Burton (1991). As originally intended and described below, the four steps are listed in sequential order; however, this rule is not hard and fast. Nevertheless, the success as well as one of the unique features of this approach lies in

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allowing sufficient student and patient choices and sufficient manipulation of task variables before more direct instruction is given by the therapist. In contrast, more traditional therapy and instruction begin with the therapist telling the patient what goal to achieve and which movement pattern to use. 3.1. Establishing

task goals

In educational or therapeutic settings, task goals are often rather discrete and should be well-understood by students or clients. However, in less structured ‘real-world’ settings, the activities of humans more often are complex interplays between themselves, and social physical environments, rather than discrete actions directed toward a single, well-defined, conscious goal. Goals, as well as the actions used to attain goals, are context conditioned. Furthermore, goals are not static, but arise from multiple conscious and unconscious intentions or motives which may abruptly change. For instance, a person may not feel hungry until the scent of food being prepared in a nearby restaurant is suddenly detected. Subsequently, this person’s primary goal may shift to the satiation of the fresh hunger pangs and away from some other goal. Freud pointed out that people often are not consciously aware of why they do some of the things they do (Yates, 1987). Goals are both unconscious and conscious intentions in everyday activities, but in the formal setting of a gymnasium or clinic, goals can and should be made explicit (see Weir, 1984 for an in-depth theoretical discussion of goal-directed behavior consistent with ecological task analysis). Thus, care is needed in establishing task goals for any person (Davis, 1989), particularly a person with severe disabilities (Henderson et al., 1989). In ecological task analysis, task goals are organized into four functional categories as modified here from Davis and Burton (1991) (see also Balan and Davis, 1993), and derived from Reed’s taxonomy of action (Reed, 1982): (a) locomotion, (b) object propulsion and reception, (cl object manipulation, and (d) postural maintenance and orientation. (Note that a fifth functional category from Davis and Burton, locomotion on object, is not included because it specifies a means to accomplish a function rather than a true function, and thus could be listed as a sub-category.) These categories are inclusive in that they account for all goal-directed human actions in which the focus is on movement outcomes - as opposed to actions the purpose of which is more cognitive, perceptual, or affective. More work needs to be done on sub-categories and descriptions of skills especially for the object manipulation category.

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These goals can be conditioned by different criteria and the intentions of the performer, and may be achieved through different anatomical parts or neural pathways. Conversely, the same mechanism may yield a variety of functional outcomes. For example, a one-hand overhand throw may be used to accomplish many specific propulsion goals, but some of the same goals may be accomplished by alternate means. Davis and Rizzo (1991) elaborate on this taxonomy and provide a more detailed rationale for its development. To the maximum extent possible, tasks goals should be specified by the social and physical environment, rather than by written, verbal, or demonstrated instructions (Balan and Davis, 1993; Davis, 1989). Although not always utilized to the extent possible, the efficacy of this approach is well-established in educational settings for all students (Balan and Davis, 19931, including those with severe disabilities (e.g., Kaiser et al., 1993) and has many advantages. First, it minimizes the difficulty some students with limited receptive language skills may have in understanding a task goal. Second, it provides an active (concrete) presentation of a goal as opposed to the more abstract modes of physical demonstrations (iconic). verbal explanations (symbolic), or written instructions (iconic/symbolic). And third, it offers performers more opportunities for choosing their own means for attaining the task goal, and thus enhances intrinsic motivation. Allowing students to structure the physical environment through selection of equipment. and setting, and/or to choose a task goal specified by the physically and socially structured environment, is self-motivating. As Edwin Locke (Locke and Latham, 19901, one of psychologists foremost researchers on goal-setting, notes, the ability to set conscious goals is a major determinant of a person’s motivation for performing tasks (Camevale et al., 1991). Research overwhelmingly supports the efficacy of having clear specific goals in task performance. Locke (1991) suggested that, “goal setting for improving task performance is one of the best established findings in management and psychology” (p. 3 11). Further, goals setting is perhaps most particularly effective in movement and sport settings (Locke and Latham, 1985). The research from more that 500 studies is summarized and integrated into Locke and Latham’s recent book documenting the importance of goals and goal setting (Locke and Latham, 1990). The method of establishing the task goal by structuring the physical and social environments is one of the features distinguishing ecological task analysis from traditional approaches which are teacher-directed. The importance of providing concrete environmental information over abstract and symbolic information in movement tasks is supported in the literature. A student may not be

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able to imitate or pretend to perform a task such as writing with a pencil when asked by a therapist, but will perform quite well when he or she actually has a pencil to use. This is a well-known phenomena in cerebral palsy (e.g.,Van der Wee1 et al., 1991) and apraxia (Brown, 1972). Van der Wee1 et al. (1991) note that such “concrete tasks differ from abstract tasks in the degree to which the required act is directed toward controlling physical interaction with the environment or with the person’s own body, as opposed to producing movement for its own sake” (p. 419). Yet, it is not uncommon to see teachers having students practice a movement without the appropriate equipment in hand. In their study, Van der Wee1 et al. (1991) found that the nine children with hemiparesis had significantly greater range of motion during a concrete ‘banging the drum’ task than when simply asked to extend the arm as far as possible. But for the 12 nursery-school children without disabilities, there was no difference (Van der Wee1 et al., 1991). Similarly, Leont’ev and Zaporozhets (1960) found patients with joint or peripheral-nerve injury, which restricted movement of the elbow or shoulder, could raise their arms progressively higher from condition (1) reach as far as possible with eyes shut, to (2) reach as far as possible with eyes open, with the arm seen against a ruled screen, to (3) reach up to a designated point on the screen, and highest in (4) reach to grasp an object. Although the importance of the social and emotional as well as physical environment is noted in ecological task analysis (Balan and Davis, 1993; Davis and Burton, 1991; Davis and Van Emmerik, 1995a), details of its structure and effect on motor development have yet to be explicated within this framework. The major role which social and emotional factors play in motor control and development is described in the literature (Rosenbaum, 1991; Wolff, 19931, but neglected by current researchers, or studied as being detached from movement (e.g., Schoemaker and Kalverboer, 1994) rather than as constituting a mutual and reciprocal relationship (see Davis and Van Emmerik, 1995a). One exception is the recent work of Schmidt et al. (1994), who found that social competence influenced how people coordinated their limbs with each other. Social psychology research on inter-subject synchrony (Bernieri et al., 1988) supports the well-known idea that a non-threatening atmosphere and group cohesiveness are conducive to learning. Further, in an area of research with infants known as social referencing, data support the importance of emotion on movement outcomes and form. Feinman (1982) defines social referencing as a “process characterized by the use of one’s perception of other persons’ interpretations of the situation to form one’s own understanding of that situation” (p. 445). For example, an infant uses the

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expression of the mother to form an opinion about a stranger. If the mother smiles, the infant understands the stranger is to be trusted. This emotional meaning is used in guiding actions. For example, in a study by Sorce et al. (1985) the classical visual cliff paradigm was used to demonstrated that infants’ utilize the emotional expressions of their mothers to guide their actions. When the year-old infants hesitated and glanced up at their mother, a happy expression prompted 14 of the 19 infants to cross the visual cliff previously avoided, but an angry or frightened expression resulted in only two attempted crossings. Further, the fear condition created a ‘negative motivational valence’: 11 of the 17 infants retreated, whereas only 3 of the 19 infants in the joy condition retreated. It has also been shown that action reciprocally affects the development of wariness of heights, an emotional response (Campos et al., 1992). Thus, the perception action coupling is also constituted by affect. More recently, Davis, Van Emmerik, Savelsbergh and Badhan (in preparation) have attempted to demonstrate the importance of emotion on movement form from a more dynamical systems theory approach. Following Warren’s affordance paradigm (Warren, 1984; see also Kelso, 1984, and below for further explanation of this approach), they hypothesized that by scaling the affective variable ‘wariness of heights’ an aspecific control variable would be scaled to a critical point and thus result in a qualitative change in movement form. Preliminary results indicated that young children do indeed change their movement form when walking a balance beam close to the support surface (where stepping off the beam would be inconsequential) versus when the beam is high above the surface and the consequences of stepping off are more serious. Complete analysis of this data is forthcoming. The area of social and emotional factors in movement is one in which movement scientists stand to learn much more from practitioners than the reverse. Therapists have always utilized their knowledge (if somewhat tacit) of the social and emotional characteristics of their patients to enhance movement forms and outcomes. Nevertheless, further research could improve this practice. The therapist’s responsibility is not to simply structure the environment to invite activity. It may be necessary for them to also assist clients in identifying appropriate goals. Henderson et al. (1989) found some evidence showing children with movement difficulties were unrealistic in the way they set goals for themselves. In this study, subjects were given feedback (sometimes intentionally inaccurate) about their performance and asked how they would perform next time. Giving specific feedback to clients for them to use in goal setting for subsequent performances is known to be valuable (Locke et al., 1968). However, as Davis and Van Emmerik (1995b) cautioned, there is a difference

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between expressing one’s judgement acting on the environment. 3.2. Allowing

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about goals verbally and selecting goals by

choices of mouement solutions

In establishing task goals, the performer identifies with a specific goal by attempting to accomplish it. As indicated in the preceding section, performers should be allowed and encouraged to choose their own solutions to the movement problem posed by the specified task goal as the best means of identifying with that goal. The freedom to seek their own solutions promotes the empowerment of the performers (Bullock and Mahon, 1992; Hutzler, 1990; Segal et al., 1993), and relies more on the intrinsic motivation of performers (Balan and Davis, 1993; Deci and Ryan, 1991). Encouraging performers to choose their own movement solutions also is likely to provide the instructor or therapist with important clues about their personal interests and attributes or characteristics which may constrain their movement options. The extensive body of literature on ‘normal’ motor development - extending back to the work of Gesell, Bayley, Halverson, and others in the early part of this century - provides data on the movement patterns which are most likely to be used by children of different ages for locomotor, propulsion, reception, and orientation tasks. Thelen (1992) warns that these normative sequences or stages should not be considered to be genetically predetermined, but acquired through functional experience by persons whose actions are constrained in similar ways, in particular by “the nature of the nuclear and cytoplasmic chemical composition of humans, their growing anatomical structures, their prenatal milieu, and the nature of their physical and social worlds” (Thelen, 1990, p. 25). Thus, variations from normative patterns could possibly be adaptive solutions to movement problems, in which case they would provide clinicians insights into the anatomical, physiological, psychological, and social constraints on an individual’s behavior which may be used in designing instruction or therapy. This nonprescriptive view of normative developmental sequences taken in ecological task analysis differs sharply from the prescriptive view taken in traditional education and therapeutic approaches where variations from normative patterns are considered defective, abnormal, or pathological - something to be modified or changed. The concept of a normal motor development sequence was a cornerstone of the major neurophysiological treatment approaches developed from the 1950s to the 1970s including neurodevelopmental treatment (NDT), Rood techniques, sensory integration (SI), Doman-Delacato patterning,

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and proprioceptive neuromuscular facilitation (PNF) (Atwater, 199 1). Even in the more contemporary approach to therapy of Carr and Shepherd (19871, “the patient’s motor performance is compared with a ‘normal’ model” (p. 71) and it is deemed “necessary to provide normal models of the tasks to be trained” (p. 78). The traditional educational and therapeutic approach of examining a person’s performance of a specified movement pattern relative to a ‘normal’ developmental sequence may provide some useful information, but is limited because the specified pattern may not be efficient for the particular person in the particular task and environmental context. In many movement education and therapy programs, assessment and intervention practices are focused on movement process and form, while movement products or outcomes are minimized. In ecological task analysis, it is recognized that the task goal is most often identified with the task outcome or product. It is the outcome of the action wherein lies the meaning for the student. Thus, a ‘top-down’ approach to assessment is recommended (Auxter and Pyfer, 1985) where the first step is to determine whether the performer is able to achieve specified criteria of functional movement products or outcomes. For example, does the person throw a ball at least 5 meters or walk 10 meters in less than 5 seconds? If the criteria are achieved, then the assessment procedure may be terminated; if the criteria are not achieved, then the choices of movement solutions or movement processes are examined more carefully, with the understanding that alternate or non-normative forms may offer the optimal solutions. In the ecological task analysis nonprescriptive approach, the importance of choice cannot be overstated. As shown in the following, there is overwhelming theoretical and empirical support indicating that students perform at higher rates and feel better about themselves when given choices. Yet in practice, very few choices are given to clients or students in the clinical and school settings. Thus, we make choice a central feature of our model. In ecological task analysis, one takes a historical-social contextual orientation, and thus has a sensitivity to current constraints placed upon children by schools and society (Davis and Van Emmerik, 1995b). We believe that in an education system in which students are led to believe they have few choices (Gatto, 19921, it is imperative for teachers and therapists to maximize decision-making opportunities for their students and clients. In the social-psychology literature, choice is defined as “the opportunity to make an uncoerced selection from two or more alternative events, consequences, or responses” (Brigham, 1979, p. 132). Research in this area has a long history, and clearly shows that the degree to which people can control their environment and make choices is positively related to their health, morale, self-esteem, and

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level of functioning (e.g., Monty and Perlmuter, 1987; Moos, 1981). As self-organizing dynamical systems, humans are inherently prone to learn about their world. There is a desire to effectively interact with one’s environment and to exert some personal control within their world. This propensity is what Condry (1987) calls intrinsic motivation and White (1959) (see also, Harter, 1978) calls ‘effectance motivation’. In the special education literature, choice has been examined as (a) a variable in motivation (e.g., Dattilo and Rusch, 1985; Deyer et al., 19901, (b) a behavior to be learned (Newton et al., 1991; Reichle et al., 1989; Wuerch and Voeltz, 1982), and (c) a concept around which to advocate services for those with disabilities (Turnbull et al., 1989). Although not extensive, the results are consistent with those reported above and are unequivocal. When given choices, students with disabilities are more productive (Berk, 1976; Lovitt and Curtis, 19691, more active and attentive (Dattilo and Rusch, 1985), and more self-confident and self-directed (Berk, 1976). Thus, acknowledging preferences and teaching choice as a decision-making process are advocated for all persons (Balan and Davis, 1993; Davis and Burton, 19911, including those with severe disabilities (Guess et al., 1985; Newton et al., 1991; Shevin and Klein, 1984). 3.3. Manipulating

pe$ormer,

environmental,

or task variables

In their original paper, Davis and Burton (1991) state that the heart of ecological task analysis is the assessment of a functional task across a range of one or more task dimensions or control variables (p. 168). These control variables are those performer, environmental, or task factors which when manipulated to some critical value may elicit a change in an individual’s movement form (Kugler et al., 1980a, Kugler et al., 1982). Thus, in ecological task analysis, movement form or developmental patterns are taken as the collective variable or order parameter in dynamical systems language (Kugler et al., 1982; Schiiner and Kelso, 1988). Movement forms may be stable or unstable, and either may be desirable depending upon the context. Stable patterns are required for consistent achievement of a task goal under a variety of environmental conditions. On the other hand, if the current movement form does not achieve the task goal a new form may be required. Instability and fluctuations may lead to new movement form which over time becomes stable and allows for goal obtainment under an even greater range of task conditions. According to Jeka and Kelso (19891, movement skill performance then, is viewed as matching the student’s intrinsic dynamic to the task dynamic.

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In the initial stages of learning a new skill, the multiple degrees of freedom available to the child may be frozen by locking limb joints in order to reduce the control problem (Bernstein, 1967; Vereijken et al., 1992a). As the skill develops, additional degrees of freedom are used, increasing the number of controllable parameters so that more fluid movements are achieved (Van Emmerik, 1992; Van Emmerik and Newell, 1990). A further refinement of the skill involves a reparameterization of the control variables in the direction of increasing stability (Newell, 1985; Schmidt et al., 1992). Thus, the manipulation of possible control variables by the instructor or therapist is believed to aid the student in discovering a stable movement pattern and to set the appropriate quantitative values on the control parameters for goal obtainment under varying environmental and task conditions. Information regarding at least four aspects of movement performance can be gained from the manipulation of control variables: (a) the critical value marking a transition to a new movement form, (b) the range of values within which the movement forms are stable or unstable, (cl the optimal value at which the movement outcome is achieved most effectively and efficiently, and (d) boundary conditions where the task goal is not achievable and new patterns do not yet emerge. Many examples of control variables which can be manipulated in educational and therapeutic settings are presented by Balan and Davis (1993) and Davis and Burton (1991). These include such things as object speed, size and weight, and task duration criteria. The concept of identifying critical and optimal values of control variables to guide therapeutic intervention has been advocated by other theorists, including Sugden and Keogh (19901, Kamm et al. (19901, Giuliani (19911, Heriza (19911, Stuberg and Harbourne (19941, and Wagenaar and Van Emmerik (1994). For instance, Kamm et al. (1990, p. 773) state that: The value of charting the pathways of change during the recovery process (as well as noting the outcome) allows the therapist to discover the points at which the system is in transition from one stable mode (perhaps dysfunctional) to another (which may be functional or another dysfunctional pattern). It is only at these junctures that the therapist may discover the control parameters, or what is pushing the system into a new realm. Control parameters may be highly specific, like CNS changes or particular muscle strength, or nonspecific, like emotional or motivational aspects, but they cannot be known a priori because of the nonlinearity of the system. While not known for certain a priori, control parameters can be hypothesized, based upon past experience, manipulated, and then verified or rejected. Thus, a

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principled program of manipulating control parameters is established. As Kamm et al. (1990) state, “the goal of treatment, according to a dynamical view, is to work on the system when it is in transition” (p. 773). Therefore, it is crucial for the therapist to observe the movement form and movement outcome in order to determine optimal performance conditions. Only in ecological task analysis assessment are both measures taken along with measures of task and environment conditions (Davis, 1993; Davis and Burton, 1991). In ecological task analysis, critical and optimal values of control variables are recommended to be reported in terms of dimensionless numbers, or ratios between the control metric and some relevant performer metric. For example, a girl may change her throwing form from a downward backswing to an upward backswing as ball diameter is scaled up to 6 inches, but it is more meaningful for the clinician to understand that the transition occurred when the ball diameter/hand width ratio just exceeded 1.0 (see Burton et al., 1992; Burton et al., 1993). Such performer-scaled or intrinsic dimension values (Kugler, 1986; Kugler and Turvey, 1987; Warren, 1984) link task or environmental variables to relevant attributes of the performer, offering insight into the performer attributes which may be shaping the movement process and product. The issue of intrinsic measures of performance will be addressed more fully later in this paper. Research in human movement has demonstrated that when an aspecific control variable is scaled to a critical value, a qualitative change in movement form occurs even when subjects attempt to maintain the initial stable pattern (Kelso, 1984). Changes in form occur, in particular, with changes in the level of energy, as when speed and force requirements are maximized or minimized. For example, when students are asked to increase or decrease their movement speed, or required to project objects at greater or lesser distances, their movement pattern will change, if a critical point is reached. Such changes in movement form can come about without the need for any additional instructions or demonstrations (e.g., Southard, 1989). In important work with patients who experienced stroke and those with Parkinson’s disease, Wagenaar (Wagenaar and Beek, 1992; Wagenaar and Van Emmerik, 19941, found that when walking speed was scaled upward, systematic changes in the phase relation between pelvic and thoracic rotations occurred. Using dimensionless analysis these researchers were able to reveal general patterns in the coordination of gait, “not found in the analysis of the absolute data”. The data on dimensionless trunk rotation showed that the rotations of pelvis, thorax and trunk become tightly coordinated between the 0.75 and 1.0 m/s speeds (Wagenaar and Beek, 1992). Wagenaar and Van Emmerik (1994) suggested that the healthy subjects in their study, but not those with Parkinson’s

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and especially those from a stroke, had adapted their movement patterns at different speeds such that the reactive forces were optimally exploited. As noted above, this exploitation is viewed as an essential stage in the development of movement skills (Bernstein, 1967; Kugler and Turvey, 1987). Increasing speed and force requirements will require the student to release more degrees of freedom and produce, for example, greater trunk rotations in an overhand throw, what some theorists in motor development called a ‘mature’ pattern of movement (e.g., Wickstrom, 1983). However, the exact opposite strategy of freezing degrees of freedom is required in the task of dart throwing. Thus, the most ‘mature’ pattern is dependent upon the task goal, a point made earlier in this paper. Further, in scaling control variables, such as speed and force, it must be kept in mind that an abrupt change in movement form (at a critical point) is dependent on the direction of scaling, a phenomenon known as hysteresis (Kelso, 1984). For example, Savelsbergh et al. (1994) found that both subjects with Down syndrome and those without disabilities, abruptly changed from a one-hand grasp to a two-hand as the size of the cubes increased. However, when the cubes were presented in a decreasing order, i.e., from large to small, neither group of subjects switched from a two-hand back to a one-hand grasp at the same cube-to-hand-size ratio. Research clearly demonstrates that scaling possible control variables effects changes in the movement form. This is believed to be an effective method of assisting each individual, as well as the instructor, in discovering optimal, critical, and boundary conditions of movement performance. 3.4. Proriding

instruction

In the preceding steps, the instructors or therapists are to assist in establishing a meaningful task goal and to provide sufficient success and challenge by structuring and changing task conditions and environmental variables. Under these steps, the instructors or therapists do not assume they know the best movement form for the student or client. However, solutions may or may not be readily apparent to the student even after several attempts under changing conditions, and it follows that the instructors should be prepared to provide other possibilities to the movement problem. As Kamm et al. (1990) stated: “The therapist can assist the patient in discovering, through natural movements, the range of possible new solutions” (p. 774). Thus, experiences and knowledge from teaching and formal preparation are to be utilized in this last step in ecological task analysis.

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Solutions from the instructor or therapist are not imposed nor offered prior to providing the students or clients choices. As stated previously, choice not only provides the student opportunities for empowerment by allowing them to make their own decision, their solutions are important information for the instructor. This latter information is utilized by the instructor in determining other solutions that are potentially more effective. Thus, an important responsibility of the therapist is to offer additional solutions through instruction. The instruction step in ecological task analysis adds flexibility to the model. Dynamical systems theory suggests that complex, open systems are not predictable in the long run (Davis and Van Emmerik, 1995a). Therefore, instructional models that are too rigid and inflexible are unable to handle unexpected changes and unpredictable consequences. Moreover, rigid and prescriptive models do not allow for individual differences in learning and teaching styles. Ecological task analysis allows the therapist to use techniques and methods other than manipulating task and environment variables. These methods include many of the traditional educational and therapeutic strategies proven effective, for example, verbal explanations and demonstrations of movement forms, corrective feedback, and passive movement. In addition to movement skill instruction, other information regarding knowledges, values, and strategies are provided by the instructor or therapist and these may be given through verbal and non-verbal instruction. Instruction would also include class management decisions such as when to terminate the movement activity and to begin others, such as clean-up or break-time. Exploration and self-discovery are essential, but are to be combined with discovery guided by the instructor or therapist in order to maximize the learning experience. Discovery through guidance in ecological task analysis means that the instructor directly provides solutions to the learners through verbal explanation and demonstration after they have had an opportunity for self-discovery. This conceptualization of guided discovery differs from Mosston and Ashworth (1986), whose model is based upon S-R theory. We note that the guided discovery style made popular by Mosston (1972) is near one end of the dimension of command-to-discovery teaching styles. The underlying dimension is decision-making, where the command style requires all decisions to be made by the instructor with an increasing number - but never all decisions - being shifted to the learner at the other end of the continuum, the discovery and divergent styles. Further, in their model (Mosston, 1972; Mosston and Ashworth, 1986), guided discovery is to occur only after many years of directed teaching. In the command-to-discovery approach, by the time students are given freedom to make their own decisions, they have been socialized to believe that

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only the instructor can make decisions or at least must do so much better (e.g., Gatto, 1992). Therefore, students often look to please the teacher rather than make their own discoveries when finally given the opportunity. 4. Intrinsic measures of movement performance Movement performance in education and therapy settings usually is assessed in terms of absolute and extrinsic measures exclusively. Some common metrics include length or distance in inches, centimeters, feet, meters, miles, or kilometers; time in milliseconds, seconds, minutes, or hours; mass in pounds or kilograms; energy in kilocalories; and frequency or number of repetitions. These all are considered to be absolute and extrinsic measures because they are based on some absolute standard extrinsic to the individual performers. Other absolute, extrinsic measures may be derived from multiple dimensions, such as velocity (distance/time) or force (mass X distance/time”). A normative approach to motor development involves the use of absolute and extrinsic measures in regards to movement products or outcomes, and extrinsic standards in regards to movement process or form. First, products or outcomes may be expressed directly as raw absolute, extrinsic scores, or may be converted to normative scores such as standard scores or percentiles, which represent the performance of a population and an individual’s relative standing within the population. Normative scores are usually calculated according to age and sometimes gender, but individual attributes more relevant to movement performance - for example, height or weight - are ignored. Thus, normative scores derived from movement product measures are not absolute because the standard varies from group to group, but they certainly are extrinsic because the standard has minimal connection to the individual performer. Second. the assessment of movement process or form relies on qualitative descriptions rather than quantitative measures. Normative, qualitative descriptions are not absolute because they are not very precise, allowing for some variation in the standard form, and they rely on judgment which can be quite subjective. However, normative, qualitative descriptions are extrinsic because, as with product norms, the standard has minimal connection to the individual performer. Movement process or form of different persons usually is assessed in the context of constant or invariant conditions, such as the one-inch cube used by Halverson ( 193 I> in his classic studies of reaching behavior in infants, or the ubiquitous 2.25inch-diameter tennis ball used in throwing studies (see Burton et al., 1993, for a discussion). Unfortunately, the physical attributes of children and adults are not invariant, at least not in terms of these absolute metrics. Thus, as

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Newell et al. (1993) suggest, the normative sequence of movement forms may be due more to these externally imposed or extrinsic constraints, which impacts each individual differently, than the actual limitations of the individual performers. One of the apparent advantages of using absolute, extrinsic measures (e.g., distance, time, frequency, or velocity) is that they are assumed to be interval or ratio scales; in other words, equal differences in the measures reflect equal differences in the property being measured. This key characteristic of interval or ratio scales allows these numbers to yield meaningful results when arithmetically manipulated. Hence, a softball throw of 100 feet is twice as far as a throw of 50 feet, and 10 pull-ups is 25% more than 8 pull-ups. However, distance and frequency are not the properties measured in these examples - the properties are more likely throwing skill and upper-limb strength, respectively. Now the question becomes: Is a person who can throw a softball 100 feet twice as skilled as someone who can throw 50 feet? Or, is a person who can perform 10 pull-ups twice as strong as person who can perform 5? Or, when dealing with a ratio scale, does a person who cannot perform any pull-ups have no upper-limb strength at all? Each of these questions must be answered in the negative. Thus, at best, these absolute, extrinsic measures are ordinal, indicating rank order or directional relationships, such as more/less skilled or stronger/weaker. There are even some situations in which absolute, extrinsic measures are nominal, endowed only with the characteristic of distinctiveness, but allowing no comparisons of the measured property. For example, a receiver in football might have fewer touchdowns than a less-skilled receiver because his quarterback has difficulty getting the ball to him, his offensive line does not give his quarterback much time to throw, or his team focuses more on a running game. Or, a person may not be able to jump as far as a less-skilled person simply because the other person is twice as tall. One solution to some of the problems inherent in absolute, extrinsic measures is to use performer-scaled or ‘intrinsic’ measures (Kugler, 1986; Kugler and Turvey, 1987; Rosen, 1978; Warren, 1984). Performer-scaled measures were offered as an important way to establish direct links between task goals and the constraints of the performer and the environment. In performer-scaled measures. a relevant task parameter is matched with a relevant performer attribute to yield a task/performer ratio in which the absolute, extrinsic units for each cancel out. These dimensionless numbers are considered to be intrinsic measures because they account for some relevant aspect of the performer. As Kugler and Turvey (1987) have pointed out, dimensionless numbers or intrinsic measurement systems can establish deviations from one’s own stability or equilibrium config-

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uration because the person-environment or action system is the referent. This is a central concept in ETA. The recognition in Western science of the importance of scaling can be traced back to Archimedes and Galileo. In 1917, Thompson explained in his book, On Growth and Form, that “it often happens that of the forces in action in a system some vary as one power and some as another, of the masses, distances or other magnitudes involved; the ‘dimensions’ remain the same . .. but the relative values alter with the scale” (Thompson, 1963, p. 25). He emphasized that “the effect of scale depends not on a thing in itself, but in relation to its whole environment or milieu” (p. 25). Thompson referred to this concept of scaling as ‘dynamical similarity’ or the ‘principle of similitude’. The substitution of dimensionless numbers for the dimensional variables that describe a problem is referred to by McMahon and Bonner (1983) as ‘dimensional analysis’. There are several examples of this approach to the study of movement. Examples include scaling the heights of objects to be negotiated by the performer’s leg length (Burton, 1990; Nagamatsu and Davis, 1992; Warren, 1984) scaling time gaps between cars when crossing a street to the time a person needs to cross the street (Lee et al., 1984), or scaling cardiac cycle, gestation period, or time for 50% of growth to lifespan in captivity (McMahon and Bonner, 1983). Some intrinsic measures involve a scaling of a performance measure to some performer attribute but, because the performance and performer units do not match, they are not dimensionless numbers. One illustration of an intrinsic measure with dimensions is the scaling of submaximal oxygen uptake by body mass (e.g., kg) or some exponent of body mass (e.g., kg . 75), or even by body mass and stride frequency (e.g., VO*/kg/stride) (Rowland, 1992). Another illustration is Burton (1990) calculating locomotor velocity by leg length per seconds rather than feet or meters per seconds. Another example of a dimensionless, intrinsic measure is the ball diameter/hand width ratio reported by Burton et al. (1993) in regards to grasping and throwing patterns. For instance, they found that as ball diameter was scaled up, the transition from a one- to a two-hand grasp occurred for ten different age-sex groups at mean ratios ranging from 0.99-1.20. Thus, despite variations in absolute ball diameter at which this transition was made from kindergarten to adulthood, the transition from a one- to two-hand grasp consistently occurred as ball diameter just exceeded hand width. Newell and his colleagues (Newell et al., 1993; Rutter, 1987) have also demonstrated the importance of using intrinsic measures in grasping activities. Rutter ( 1987) included in his study 11 children age 7 to 14 years and 34 adults

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aged 18 to 59 years old. Subjects were required to grasp 14 spheres ranging in size from 3 mm to 180 mm in diameter under the action categories of immobilization, manipulation, displacement and projection. The intrinsic or body scaled measure was finger span, index to thumb, divided by sphere diameter and was compared to the extrinsic measure of the number of digits against sphere diameter. As predicted, absolute measures separated the children and adults, but on the intrinsic measure they were similar. In other words, the number of fingers used to grasp the object were similar for adults and children when the relative size of the object was the same. Rutter (1987) also identified the point of transition from one grip pattern to another, and a boundary point at which subjects were unable to successfully achieve a stable grip. Significantly, these points were found to be similar for subject groups of both adults and children, but were different for the different action categories. Thus, body scaling alone does not predict the movement form, rather, the task goal must also be taken into consideration. Further demonstration of the importance of intrinsic measures is seen in the comparison of two approaches to the study of grasping activities of children with Down syndrome. In one approach Moss and Hogg (1981) first examined how preschool children with Down syndrome and similar aged children without disabilities grasped rods which varied in diameter, but not weight, and how they placed them in vertical and horizontal positions as fast as possible. These researchers found that children with Down syndrome did not show the same increased use of precision grips - going from an ‘immature’ power grip (transverse palmar - all fingers around the rod) to a more ‘mature adult’ digital grip of rod between index finger and thumb - as children without disability. However, Moss and Hogg (1981) noted differences in hand size between the two groups and suggested that subjects with Down syndrome’s “infrequent use of digital grips may partly be accounted for by anatomical factors” (p. 41-42). It is well known that hand size and finger lengths of children with Down syndrome differ from a control group without disabilities (Chumlea et al., 1979). Thus, in a subsequent study (Hogg and Moss, 1983) several measures of hand size were taken from 24 pairs of preschool children matched on mental age with subjects with Down syndrome, and results of these measures confirmed the earlier work showing size differences. Unfortunately these researchers did not use intrinsic measures to examine the effects of these differences on grasping. Hogg and Moss (1983) concluded that, “the use of digital grips is directly related to the size of the object manipulated and in the present case the relation between rod size and use of digital grips was very similar for both groups” (p. 197), but their analysis did not verify their conclusion.

In a more recent study. Savelsbergh et al. (1994) confirmed the Hogg and Moss (1983) conclusion with a quantitative analysis between the hand and object size relationship and movement form. We examined the transition from one-hand to two-hand grasping, and the reverse transition in a simple task of picking up different size cubes. Subjects with Down syndrome and a group of subjects without disabilities of the same chronological age were asked to pick up the cubes presented in two size orders: going from large to small and from small to large. First, we found no group differences on any of the results except the measure of absolute hand size, as expected. showed that children with Down syndrome have smaller hands. Second. we confirmed body scaling effects only when the boxes were presented in an increasing size order. All subjects switch from a one-hand to a two-hand movement pattern at a similar critical size ratio. However. when the objects were presented in an order from large to small, no consistent ratio among subjects was found for either the subjects with or without Down syndrome. This effect has been termed hysteresis (Kelso, 1984). In other words, subjects showed that when the size of the cube increased to a critical point, a switch to a two-handed grasp was necessary in order to successfully pick it up. In contrast, when the size of the cube decreased, it was not necessary to switch from a two-hand to a one-hand grasp in order to achieve the task goal (Savelsbergh et al.. 1994). An important finding was that no differences were found between subjects with Down syndrome and those without in regard to the scaling effects. Perhaps even more efficacious than body-scaled measures is the use of action-scaled measures. Burton (1990) extended Warren’s paradigm to subjects with developmental disabilities (Warren, 1984). His locomotor task required subjects with and without disabilities to traverse a set distance of 9.76 meters as rapidly as possible. Baseline running speeds were obtained and related to leg lengths as an intrinsic action-scaled measure. Subsequently, four sets of barriers of various heights were placed in front of the subjects and they were allowed to go over or under however they choose. The height of the barriers were body-scaled. Several other measures were taken and a number of important findings obtained. Burton ( 1990) showed that older children ran faster than the younger children without any barriers, as expected. However, when running speed was scaled to leg length rather than absolute distance, the movement time differences disappeared. On this basis, Burton (1990) neutralized physical size with respect to running speed and was then able to examine other factors relative to the task goal. For example, a different picture emerged when the more complex environmental condition of having barriers was considered. With the four barriers

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included in the course, the movement efficiency ratio (ratio between the movement time with the barriers over times without the barriers) for the younger children was lower compared to the older children. This indicated that the younger children required more time in comparison to their own simple movement times (without barrier) to perceive and act on the constraints of the environment relative to their own personal constraints. The younger children with developmental disabilities, in turn, were less efficient than the children of the same age without disabilities in regard to negotiating the barriers. Another important finding was that all three groups of children exhibited the same critical height for the locomotor transition from going over to walking under the barrier demonstrating the value of using body-scaled measures. In a modification of this paradigm, Nagamatsu and Davis (1992) tested 68 children without disabilities in the age range of 5-12 years, demonstrating that action-scaled measures can be used to normalize performance measures at least across the ages (5-12 years) of their subjects. This is an improvement over the use of age norms. In this study, subjects ran six stride lengths (rather than a set distance as in Burton, 1990) and the distance traversed was measured. Subsequently, subjects ran the same measured distance while negotiating one barrier, a 2 cm strip of crepe paper, placed at five different body scaled heights for each of five sets of trials. As in the Burton (1990) study, subjects were allowed to choose their own movement form in either going over or under, but not touching, the barrier. Results indicated two primary movement forms were used: (a) jumping over and (b) ducking under and that these forms also best achieved the task goal specified by the researchers - running speed. The other movement patterns which more often occurred near the critical points (points of transition from going under to going over), took considerably more time. Of interest to the present concern of therapeutic method is the finding that the fastest time occurred by jumping when the barrier was at the highest body scaled height. This means that even though the researchers instructed the subjects to run their fastest on each trial, it was the barrier height itself (the physical environment) which led to the fastest times. In other words, when some subjects determined that the task goal for them was to jump over, they correctly perceived that a higher barrier meant that they had to run even faster in order to generate the force needed to accomplish their goal of jumping over. However, there are important limitations to be noted in the above studies. Body scale primarily accounts for the geometric variables leaving the kinematic variables unmeasured in any direct way. Another approach to scaling, allometry, relates one biological form or process (Y > to another (X) through an empirical power function: Y = aX ‘, where the b-exponent represents the relative rates of

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change between X and Y. This formula can be traced back to Huxley’s work on differential growth (Huxley, 1924, Huxley, 1932). Most often, both X and Y are physical variables, but some researchers have used allometry to examine the relationship between physical and performance variables. Asmussen and Heeboll-Nielsen (1955) used the allometric equation to determine what power of standing height best estimated various physical and performance measures in 7to 16-year-old boys. The performance measures included maximum oxygen uptake, running speed, vertical jump, and upper- and lower-limb strength. The predictions of power relationships between standing height and performance are presented in a chapter in the classic Textbook of Work Physiology of Astrand and Rodahl(1986). Dimensional analysis and allometry offer mathematical approaches to scaling performance measures in relation to physical parameters which can be applied to an individual, across a group of individuals, or even across species. However, Yates and Kugler ( 1986, p. 1024) argue that, the allometric approach attracts too much attention to a scaling based on the single variable, size. It seems much more likely to us that in a complex system such as the mammalian organism, both mechanical and thermodynamic quantities will be fundamental, and that to locate a ‘world point’ in state or event space will require use of a reference frame with multiple coordinate axes (dimensions), one for each independent, fundamental quantity, going beyond the three spatial coordinate axes. Indeed, two people may have the same absolute, extrinsic performance values or the same intrinsic or performer-scaled values, but they may have dissimilar movement dynamics. In short, there may be qualitative differences in their action systems which may not be exposed by extrinsic or even intrinsic measures of performance. How, then, might such potential differences be identified? One method was discovered by Froude, who faced a similar problem as he sought to apply what he learned from experiments with scaled-down models of full-sized ships (for a more complete discussion of Froude, see Alexander, 1992; McMahon and Bonner, 1983); that is, he needed to ensure that the scaled-down and full-sized designs were dynamically similar. Froude observed that when two proportionally similar but different-sized models of the same hull were pulled through water at the same speed, they produced different patterns of wave turbulence. He also discovered that he could find a particular speed for each model at which the wave patterns were almost the same. Froude’s conclusion that hulls are dynamically similar, at least in regards to drag produced by waves, when wave

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patterns are similar, is expressed in the dimensionless equation, velocity */(gravitational acceleration X length), now known as the Froude number (McMahon and Bonner, 1983). Applying Froude’s ideas to animal locomotion, Alexander (1993) postulated that “systems of different sizes that are subject to gravitational forces can move in dynamically similar fashion, only when their speeds are such as to make their Froude numbers equal” (p. 4). Specifying the length parameter in the Froude equation as leg length or hip height, Alexander and Jayes (1983) found that at the same Froude number, at any given magnitude, mammals of different sizes including dogs, cats, sheep, wildebeests, horses, gazelles, camels, and rhinoceroses - tend to move their legs in the same phase relationships, to make the same gait transitions, to exert similar patterns of force on the ground, to have the duration of their stance phase to be the same, and to take strides in similar proportions to their leg lengths. For example, the transitions from walking to trotting and from trotting to galloping occurred at Froude numbers of 0.5 and 2.5, respectively. Exact dynamic similarity was not attained nor expected by Alexander, because the animals differed in geometry and proportions. The driving force behind these amazing similarities in movement dynamics at the same Froude values is the need for animals, including humans, to minimize the amount of energy expended during locomotion and other movement tasks (Alexander, 1993; Sparrow, 1983; Sparrow and Irizarry-Lopez, 1987; Vereijken et al., 1992b). Alexander (1991) pointed out that two systems are dynamically similar only if they have the same ratio of kinetic energy (mass X velocity’/2) to potential energy (mass X gravitational acceleration X height). This ratio reduces to velocity 2/2 (gravitational acceleration X height), which is proportional to a Froude number (i.e., half or 50%). In this section on intrinsic measures of movement performance, the problems inherent in absolute and extrinsic measures and the virtues of intrinsic or performer-scaled measures were discussed. Intrinsic measures are clearly more consistent with the tenets of ecological task analysis, offering more explanatory power in regards to the relationship between performer attributes which are used as a scaling factor - such as hand width, leg length, or body mass - and the action performed. Hence, intrinsic measures, including dimensional analysis and allometric approaches, are recommended to be used instead of extrinsic measures. Finally, Froude numbers were discussed as providing a potential method of quantifying the global dynamics of an individuals action system in terms of a dimensionless number. Practitioners may be able to use Froude numbers to categorize individuals according to the similarity of their global dynamics, and

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to provide similar interventions for persons with in the same categories. Such categories of action may prove to be more meaningful than other classification systems, including the functional classification systems which have recently been espoused in educational and therapeutic literature. In spite of the limitations noted above, body-scaled and action-scaled measures without precise quantification may prove useful to the therapist. Indeed, they are already quite common practices in therapy and in youth sports where the same equipment comes in a variety of sizes to be matched to the size of the client, student, and athlete (Haywood, 19931. Appropriately sized equipment is shown to improve performance (e.g., Ward and Groppel, 19801. Body-scaling simply means using an anatomical measure to determine the geometric dimensions of equipment and surfaces most appropriate for each particular student. For example, setting the obstacle to be jumped at knee-height of each student rather than at an absolute height accommodates all heights. As noted, the practice of using one’s own anatomy for measurement has a long history.

5. Summary In summary, ecological task analysis is perhaps the most comprehensive and detailed application of dynamical systems and related theories to the assessment and intervention of movement skills which appears to date. First presented by Davis and Burton (1991), it is expanded in subsequent writings (Balan and Davis, 1993; Burton and Davis, 1992; Davis and Van Emmerik, 1995a, Davis and Van Emmerik, 1995b). The four steps or procedures of the model establishing a task goal, providing choices, identifying and manipulating task (control) variables, and providing instruction - are described. Ecological task analysis is designed to gain insight into the dynamics of movement behavior by examining the interacting constraints (both limitations and enablements) of performer, environment, and task. It contrasts with the traditional neurophysiological approaches in several ways. The latter are prescriptive in orientation and assume the same ‘normal’ model is applicable to everyone. Conversely, ecological task analysis is based on the premise that there are many possible solutions to a task as determined by the unique mutual and reciprocal relationship between performer attributes, environmental conditions, and the goal or intent of the action. There are strong theoretical and practical rationale, as well as empirical support for the four steps in the ecological task analysis model. Details of this support are given here along with a description of the unique features and the

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several advantages this model holds over traditional models of assessment and intervention. Ecological task analysis is believed to be applicable to a wide variety of educational and therapeutic settings, and may be particularly effective in a clinical setting.

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