Leaning On More Than Task Manipulation
Over the past 2 to 3 months, coaches have been reaching out with questions about constraints manipulation, which has coincided with a surge of constraints-led questions from several individuals involved with The Movement Academy here at Emergence. It is encouraging to see the interest from coaches who want to learn more about purposeful constraints manipulation in practice where consideration is given to what opportunities for action (affordances) “might” emerge when certain constraints are manipulated.
Before we go any further, we think it’s pertinent to briefly discuss a few key principles underpinned by ecological dynamics that can support coaches in designing learning environments with the potential to develop adept problem-solvers in sport. First, constraints are viewed as informative boundaries that can shape, guide, or nudge the reorganization of complex adaptive systems (Renshaw et al., 2019), which are open systems continuously exchanging energy with the environment (meaning they can influence the environment and be influenced by it too). Conceptualizing constraints as informative boundaries is important because actions are not caused by constraints; rather, some actions are excluded by constraints. In this way, constraints act as information influencing the behaviors that are possible. Additionally, it’s critical to acknowledge constraints do not act in isolation but coalesce together to shape behavior, meaning that the continuous interactions of the individual, the task, and the environment, can give rise to the skills that emerge during practice. How does this happen? Constraints interact together to shape perception-action as an individual negotiates the environment to use available affordances. Furthermore, it’s important to understand that they operate over long and short timescales. For example, a young player’s body components (motor system degrees of freedom) change slowly over longer timescales, through maturation, development, and effects of regular training, impacting their coordination and control when solving problems in sports. In contrast, in the timescale of competitive performance, the perception of affordances, such as gaps emerging and closing in the defense might offer run-through ability for an offensive team. In other words, in practice and preparation: Context is everything! Consider that a small change in constraints can lead to significantly different outcomes; movement behavior is always dependent on the circumstances where a player’s entangled perceptions, intentions, and actions are emerging.
Now to the constraints-led approach (CLA) to coaching, which our friend Rob Gray summarized nicely as “Using constraints in a highly specific way to achieve specific objectives.” Rob’s simple yet concise view of the CLA is why we italicized the word “purposeful” early in this blog post. The intent behind the constraint applied (e.g., encouraging the exploration of new movement solutions or de-stabilizing non-functional movement solutions) is crucial in an ecological-driven approach to constraints manipulation. The CLA isn’t a new pedagogical approach to coaching in sports; in the mid-’90s, Professor Keith Davids called into question how professionals across the movement community conceptualized coordination and the control of action in sports. Davids illuminated the importance of viewing constraints shaping the emergent behavior of athletes in sports, along with prioritizing the athlete’s relationship with a specific performance context saying, “Coordination solutions emerge to satisfy interacting constraints (Davids et al., 1994).” Building on the original representative design idea by the psychologist Egon Brunswick (1955), who advocated for the study of organism-environment relations in psychological research and designing key features of the environment into experiments, Pinder and colleagues (2011a; 2011b) through the framework of ecological dynamics, re-configured representative design, as representative learning design (RLD) in sport performance contexts. Representative learning design embeds players within contextually sensitive practice environments where they interact with perceptual variables that constrain or channel functional behaviors where greater action-fidelity (Stoffregen et al., 2013) is maintained. Constraints-led coaching applied through representative learning design provides coaches with a framework for purposely manipulating constraints.
Now that we have briefly covered some of the underpinning ideas: let’s return to how constraints can be applied more holistically to guide our athletes in solving movement problems in sports. The overarching thread in the conversations we’ve recently had with coaches is the tendency to only look at manipulating task constraints in practice. Task constraints are reflective of important variables like game rules, equipment, playing area dimensions, boundaries, opponents, and teammates, which are not only powerful variables to manipulate, but likely some of the easiest. Manipulating task constraints is important, but we must investigate the complete movement problem-solving process in person, using film analysis, or both to potentially understand why something did or did not occur. Discussions with our players also help us to gain a better understanding of why certain behaviors are emerging.
For example, you may notice a baseball player struggles to get hits in night games, a soccer player who has difficulty playing on damp surfaces, or a basketball player who battles playing slightly fatigued. If this is the case, they would likely benefit from being embedded within these situations, so they gain exposure. Here are just a few examples of individual or environmental constraints that you might look to manipulate in practice.
- Change the start time of practice to impact the lighting (e.g., morning to afternoon or afternoon to nighttime)
- Change the start time of practice to impact the moisture (e.g., dew on the ground in the morning and at night)
- Change the start time of practice to impact the temperature or wind conditions (e.g., morning to afternoon or afternoon to nighttime)
- Change the physical location of the practice (e.g., the opponent’s city limits, the direction concerning the sun, etc.)
- Invite parents, friends, or opponents to the practice
- Practice on different surfaces, which impacts their footing, how the ball travels on the surface, etc.
- Have players perform skills under fatigued states (e.g., shooting free throws in basketball after playing defense followed by a fast break or serving after a long rally in tennis)
The above manipulations will likely induce pressure, anxiety, and/or fatigue, which are key performance inhibitors (KPI’s) that shape their emergent behavior. Again, it is worth mentioning the interacting constraints give rise to the movement solution that emerges, and it is wise to lean on more than task manipulation to guide learners toward more functional movement solutions. As players become more attuned by going through the process of solving contextual movement problems, their skills adapt over time. In closing, from a constraints-based perspective, motor learning can be viewed as searching for, discovering, and exploring more functional performance solutions that are individually relevant, known as skill adaptation.
Tyler Yearby, M.Ed. | Co-Founder & Co-Director of Education, Emergence
Keith Davids, PhD | Professor of Motor Learning, Sheffield Hallam University
For more reading:
Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. https://doi.org/10.1037/h0047470
Davids K., Handford C., & Williams M. (1994). The natural physical alternative to cognitive theories of motor behaviour: an invitation for interdisciplinary research in sports science? J Sports Sci.12(6):495-528. doi: 10.1080/02640419408732202. PMID: 7853448.
Pinder, R. A., Davids, K., Renshaw, I., & Araújo, D. (2011a). Representative learning design and functionality of research and practice in sport. Journal of Sport and Exercise Psychology, 33(1), 146-155. https://doi.org/10.1123/jsep.33.1.146
Pinder, R. A., Renshaw, I., Davids, K., & Kerherve, H. (2011b). Principles for the use of ball projection machines in elite and developmental sport programmes. Sports Medicine, 41, 793-800. https://doi.org/10.2165/11595450-000000000-00000
Renshaw, I., Davids, K., Newcombe, D., & Roberts, W. (2019). The Constraints-led approach: Principles for sports coaching and practice design (1st ed.). Routledge. https://doi.org/10.4324/9781315102351
Stoffregen, T.A., Bardy, B.G., Smart, L.J., & Pagulayan, R. (2003). On the Nature and Evaluation of Fidelity in Virtual Environments. In L.J. Hettinger & M.W. Haas (Eds.), Virtual and Adaptive Environments: Applications, Implications and Human Performance Issues (pp-111-128). Mahwah, NJ: Lawrence Erlbaum Associates.
Keith Davids, PhD | Professor of Motor Learning, Sheffield Hallam University