BROADCASTS.com

SHOW NOTES\n\n00:00 - Intro\n03:44 - Shawn\u2019s Background\n08:05 - Teaching Without Teaching\n11:26 - What is Ecological Dynamics?\n17:38 - Scaling Complexity, Manipulating Constraints\n30:54 - The Zone of Optimal Challenge\n35:24 - The Performance is the Screen\n39:34 - Aliveness\n46:57 - Task Constrained Learning\n50:16 - Zooming In and Zooming Out\n54:48 - Attention, Intention and Calibration\n1:15:01 - Balancing Extrinsic and Intrinsic Attention\n1:25:23 - Becoming Movement Problem Solvers\n\nHow do humans learn to move?\n\nBe it in sports, fitness, trade skills, performance arts or just everyday life, the process of acquiring skill in movement is something that we all must go through.\n\nFor decades now it seems like the most widely adopted method of motor learning has been to apply a reductionist mindset to skill acquisition.\xa0\n\nThe idea is to improve our movement by practicing isolated component skills with the hopes of perfecting patterns that can then be integrated back into the bigger picture task.\n\nFor instance, if you wanted to get better at playing basketball you might individually practice free throws, three pointers, lay ups, etc\u2026 with the goal of having perfect form on every repetition.\n\nIf you want to learn parkour, you might have an instructor give you detailed explanations of each vault: how to angle your hands, where to jump from, what each foot is doing at the beginning, middle and end of the vault, where your eyes are trained, your breathing, etc\u2026\n\nThe problem is that this approach doesn\u2019t address the variability of context and constraint.\n\nIn a real game of basketball, no two shots will be under the same conditions, and the number of possible conditions to shoot from is far too vast to practice each individually.\n\nEven further, this method is directly at odds with how humans actually develop motor skill because it tends to rely on the prefrontal cortex which is great for organizing propositional knowledge but ineffective for performing skillful, adaptive movement.\n\nPerhaps you\u2019ve experienced \u201cparalysis by analysis\u201d: freezing up or being unable to act as a result of trying to mentally organize and process too many individual component actions..\n\nAs it turns out, it is far more effective to gain movement skill by engaging in tasks built around complex problem solving.\n\nIt's fairly obvious if you think about it. As babies, we don\u2019t learn to locomote because of our parents detailed instruction of contralateral crawling patterns.\xa0\n\nWe learn to crawl because we need to solve the problem of not being able to play with that interesting thing on the other side of the room.\n\nThe process of solving that problem, and other problems like it, ingrains those movement solutions into us and before long we are solving new problems to reach things that are higher up which opens the doors to climbing and moving bipedally.\n\nSo how do we apply this process of learning to our own movement practice, or even to the practice of elite level athletes?\n\nWell on today\u2019s episode of the Evolve Move Play Podcast we\u2019ve got Shawn Myszka joining us to talk about just that.\n\nHis work has centered around using ecological dynamics to improve not just how top NFL players move, but how they utilize their capacities for attention and intention to become better problem solvers on the field.\n\nIf you\u2019ve been to one of our retreats, you\u2019ve most likely heard us talk about how it\u2019s not patterns that are important, but solutions.\n\nBy training at the highest level of complexity possible to achieve our goals and implementing a task based constraint approach to learning, we can effectively tap into our innate motor learning systems and streamline the process of skill acquisition.\n\nWe really enjoyed having Shawn on the show and hope to have him back soon. Let us know what you think in the comments below and don\u2019t forget to like, share and subscribe!\n\nLearn more about Shawn at:\n\nTwitter: @movementmiyagi\nOnline: emergentmvmt.com