Creating Movement Problem Solvers with Shawn Myszka: EMP Podcast 37
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How do humans learn to move?
Be 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.
For decades now it seems like the most widely adopted method of motor learning has been to apply a reductionist mindset to skill acquisition.
The 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.
For instance, if you wanted to get better at playing basketball you might individually practice free throws, three pointers, lay ups, etc… with the goal of having perfect form on every repetition.
If 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…
The problem is that this approach doesn’t address the variability of context and constraint.
In 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.
Even 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.
Perhaps you’ve experienced “paralysis by analysis”: freezing up or being unable to act as a result of trying to mentally organize and process too many individual component actions..
As it turns out, it is far more effective to gain movement skill by engaging in tasks built around complex problem solving.
It’s fairly obvious if you think about it. As babies, we don’t learn to locomote because of our parents detailed instruction of contralateral crawling patterns.
We 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.
The 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.
So how do we apply this process of learning to our own movement practice, or even to the practice of elite level athletes?
Well on today’s episode of the Evolve Move Play Podcast we’ve got Shawn Myszka joining us to talk about just that.
His 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.
If you’ve been to one of our retreats, you’ve most likely heard us talk about how it’s not patterns that are important, but solutions.
By 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.
We 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’t forget to like, share and subscribe!
00:00 – Intro
03:44 – Shawn’s Background
08:05 – Teaching Without Teaching
11:26 – What is Ecological Dynamics?
17:38 – Scaling Complexity, Manipulating Constraints
30:54 – The Zone of Optimal Challenge
35:24 – The Performance is the Screen
39:34 – Aliveness
46:57 – Task Constrained Learning
50:16 – Zooming In and Zooming Out
54:48 – Attention, Intention and Calibration
1:15:01 – Balancing Extrinsic and Intrinsic Attention
1:25:23 – Becoming Movement Problem Solvers
[Playing with Movement with Todd Hargrove: EMP Podcast 29]
[The Body as Poetry with Jozef Frucek: EMP Podcast 28]
[How to Solve Problems and Cultivate Wisdom with John Vervaeke: EMP Podcast 25]
Learn more about Shawn at:
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