How We Learn to Move

1. The Myth of the One "Correct" Technique: Embrace Variability

Skilled performance did not involve one correct movement technique.

Challenging repetition. The traditional view emphasizes rote repetition of a single, "correct" technique. However, skilled performance involves using a slightly different technique with every execution. Nikolai Bernstein's work with blacksmiths demonstrated "repetition without repetition," where skilled workers achieved consistent outcomes through variable movements.

Inter- and intra-variability. Skillful movers don't coordinate movements identically across individuals (inter-movement variability) or within the same individual (intra-movement variability). Coordination profiling reveals distinct movement patterns among elite athletes, highlighting the individuality of skill. The idea of the one correct technique is a myth.

Anti-repetition revolution. The key to becoming skillful is not strict repetition but learning to produce the same outcome using different movements. What happens after a movement starts is as important, if not more, than the planning done beforehand. This adaptability is crucial for handling the ever-changing conditions of real-world performance.

2. Variability is Essential: Adaptability and Injury Prevention

With variability comes adaptability!

Variability as a design feature. The human body is built to produce and detect variation. Heart rate variability (HRV) and microsaccades are examples of how inconsistency is beneficial. Variability allows us to adapt to changes in our environment, both internal and external.

Adaptability through degeneracy. Biological degeneracy, where structurally dissimilar components perform similar functions, provides multiple solutions to achieve the same goal. This redundancy makes us robust and adaptable, allowing us to compensate for failures and adapt to changing conditions. Rafael Nadal's ability to execute between-the-legs shots exemplifies this.

Injury prevention. The variability-overuse hypothesis suggests that injuries occur when there is not enough variability in movement to allow for adaptation. Encouraging movement exploration and variability can reduce the risk of injuries like runner's knee by distributing stressors and promoting compensation. Variability is a protective mechanism.

3. Self-Organization: Movement Without a Boss

The workers are organizing themselves using only the information available to them, without the need for a boss.

Challenging the central executive. The traditional view posits a "Central Executive" in the brain that issues commands to move. However, self-organization suggests that order arises from interactions between lower-level components, without a central controller. A flock of birds exemplifies this, where coordination emerges from local interactions.

Perception-action coupling. In self-organization, actions are directly controlled by what we perceive, without the need for processing and analysis. Decisions emerge from this coupling. A bird in a flock adjusts its movements based on perceptual information about its neighbors, not from explicit instructions.

Training implications. Self-organizing systems require training that integrates perception, action, and decision-making. Context is crucial, and training should focus on specific action problems rather than general abilities. This approach is more adaptable and robust to errors.

4. Constraints: Guiding Self-Organization

A constraint is something that eliminates certain possibilities or options for action.

Newell's constraints model. Constraints shape movement by eliminating options, not prescribing actions. There are three types of constraints: individual (physical properties), environmental (general properties of the world), and task (specific to the skill). Coaches have the greatest control over task constraints.

Simplifying degrees of freedom. Constraints help solve the problem of choice by reducing the number of options. Bernstein proposed "freezing degrees of freedom" by rigidly fixing joints or coupling their movements. Coaches can also use instructional constraints to guide players.

Amplifying errors and creating opportunities. Constraints can amplify errors, making ineffective movement solutions more apparent. They can also create new action opportunities by changing equipment or improving individual constraints through strength and conditioning. Variability can be introduced to encourage exploration and adaptability.

5. Embodied Perception: Seeing What We Can Do

Perhaps the composition and layout of surfaces constitute what they afford.

Challenging accurate representation. Traditional perception assumes our visual system accurately registers physical properties of the world. However, embodied perception argues that perception is scaled by our ability to act. Athletes perceive objects as bigger when they are performing well.

Gibson's affordances. Surfaces afford opportunities for action. We perceive what a surface affords (what we can do with it) rather than just its physical properties. Gaps look "pass-through-able," and opponents look "tackle-able."

Body and action capacity scaling. Perception is calibrated to our action capacity. We incorporate our own abilities when picking up information from the world. Pregnant women adjust their perception of doorway width based on their changing body size.

6. Learning as Search: Navigating the Perceptual-Motor Landscape

Dexterity is the ability to find a motor solution for any external situation, that is, to adequately solve any emerging motor problem.

The perceptual-motor landscape. Learning a new skill is like searching through a landscape of possible movement solutions. This landscape has valleys representing stable, attractive solutions. These attractors influence our coordination tendencies.

Intrinsic dynamics and attractors. We all have coordination tendencies, or intrinsic dynamics, that make certain movement solutions more stable and easier to execute. These attractors provide stability and prevent injury but can also limit exploration. Tim Tebow's throwing mechanics exemplify this.

Breaking free from attractors. Changes in constraints are needed to break free from attractors and explore the perceptual-motor landscape. Effective coaching involves designing practice that encourages athletes to climb out of attractor valleys and explore new solutions. This requires accepting the complexity and individuality of skill acquisition.

7. Constraints-Led Approach: Designing for Exploration

We are trying to jump in and be the boss when the company does not have one!

Principles of the CLA. The Constraints-Led Approach (CLA) involves manipulating constraints in practice to:

• Destabilize existing movement solutions
• Encourage exploration and self-organization
• Amplify information and invite affordances
• Provide transition feedback about the effectiveness of the search

Connection ball example. Using a connection ball in baseball pitching exemplifies the CLA. It destabilizes the existing movement, encourages exploration, amplifies information about arm-body coordination, and provides transition feedback through the ball's trajectory.

Small-sided games. Small-sided games in sports like soccer and basketball are another example of the CLA. They reduce the number of players and/or the size of the playing area, increasing interaction opportunities and promoting self-organization.

8. Differential Learning: Amplifying Variability for Skill

If we want to have extraordinary performance we need to train extraordinarily.

Adding random variability. Differential learning involves adding random fluctuations to the training environment to amplify the inherent variability in an athlete's movement. This promotes stochastic resonance, pulling out the signal from the noise.

Perturbing the system. Unlike the CLA, differential learning doesn't aim to guide athletes to specific movement solutions. Instead, it perturbs the system to allow the performer to gain information about the solution space. The goal is to explore all corners of the solution space and learn to adapt.

Optimal level of noise. The amount of variability added should be tailored to the individual athlete, inversely related to their inherent variability. Factors like the distance from the existing solution, age, and skill level also influence the optimal level of noise.

9. Good vs. Bad Variability: Refining Movement Solutions

With practice we initially show very large improvements from day to day, then we hit the plateau where things get tough and many of us put that guitar back in the closet or stop showing up for those karate lessons.

Defining a "good" solution. A "good" self-organized movement solution depends on the performer's skill level. Novices may benefit from freezing degrees of freedom for initial proficiency. However, optimal solutions involve freeing degrees of freedom and developing motor synergies.

Motor synergies. Motor synergies are movement solutions where functional co-variation between degrees of freedom stabilizes performance outcomes. They allow athletes to adapt to changing constraints. Developing motor synergies is key to adaptive optimality.

Uncontrolled manifold analysis. Variability is "good" when it keeps the performance outcome stable and successful, and "bad" when it destabilizes the outcome. Skillful performance involves a relative increase in good variability and a decrease in bad variability.

10. Creativity: Emergence Through Action

Perhaps the composition and layout of surfaces constitute what they afford.

Challenging the cognitive view. The traditional view sees creativity as a passive, cognitive process. However, creativity arises from a symmetrical, coupled interaction between the individual, task, and environmental constraints. Acting is a central part of the creative process.

Fosbury Flop and Brill Bend. The simultaneous emergence of the Fosbury Flop and Brill Bend highlights the role of changing constraints in creativity. Both high jumpers were influenced by new equipment and a willingness to explore different solutions.

Promoting creative solutions. Coaching methods that encourage self-organization, exploration, and variability also promote creativity. Small-sided games, differential learning, and constraints manipulations can inspire athletes to explore a wider range of movement solutions.

11. Youth Coaching: Prioritizing Fun and Exploration

They (the kids) struggle to develop motor skills, they don’t develop game performance and they don’t develop fitness.

The problem with cones. Traditional youth coaching often involves isolated, repetitive drills that decouple perception from action. Dribbling through cones exemplifies this, removing decision-making and purpose. This approach is boring and ineffective.

Task simplification. Instead of task decomposition, youth coaching should prioritize task simplification. This involves scaling down the skill while keeping its basic structure intact. Playing tag instead of dribbling through cones is an example.

Equipment scaling. Scaling equipment, such as using smaller racquets and lower compression balls in tennis, allows young athletes to develop more effective movement solutions. It avoids the problems associated with decoupling perception from action.

12. Expertise: Adaptation, Not Automaticity

Learning is about attending to things, rather than acquiring the knowledge that absolves us of the need to do so...

Challenging skill acquisition. The traditional view emphasizes "skill acquisition," where we acquire knowledge and store it in our brain. However, expertise is about building a stronger, more effective connection with our environment. It's about adaptation, not acquisition.

Direct learning. Direct learning involves three types of changes: education of attention (using better information), education of intention (changing goals), and calibration (adjusting the relationship between information and movement). These changes occur through interaction with the environment.

Skills vs. habits. The goal is to develop skills, not just habits. Skillful behavior is intelligent, responsive, and adaptive. It involves innovation, whereas habits involve sheer repetition. We want to develop adaptive, variable problem solvers, not robots executing stored programs.

Last updated: February 21, 2025