The ball bounces up and down on your foot and it feels perfect. You’re connected with the ball somehow—just by tapping it and letting gravity retrieve it. You’re concentrated but you can’t really describe how you’re controlling the ball. And it wasn’t so automatic years ago. How did it get to be this easy?
Think like a roboticist. Imagine programming a machine to learn foot juggling. How would you solve this problem?
You might ask a robot to try various poses to lift its legs so it’s most stable. Maybe you want it to judge the spin of the ball in the air. Maybe the trajectory. Maybe you want it to find the optimum leg angle of contact for each particular environment. Do you use trial and error or gather more data to run fast simulations after each kick?
Humans learn movements (a.k.a. motor skills) in two ways:
1. By revising models of ourselves, other objects, and the environment
2. By revising commands to optimize accuracy, speed, and energy
The first mechanism we use is to create predictive models for our movement. We have models of ourselves, objects, and other living things in our impressive model repertoire. Every time you lift that coffee cup to take a sip, you’re drawing upon the model you’ve made of your arm, your wrist, your tired fingers, and your particularly thick porcelain coffee cup. You’re even using a model of how much your last sip weighed in order to expertly drink again. Of course, none of these models slip into your consciousness because you’d be overwhelmed at breakfast—It’s automatic.
To learn is to refine your models to have less error.
In a study, people watched a video of a basketball free throw shot and were asked whether the ball would go into the basket. Unsurprisingly, expert basketball players did better than novices. Players predicted outcomes significantly better than chance from footage of a free throw that stopped before the ball was even released. These players have created predictive models based on body movement not on ball trajectory—a set of models that give them an edge when a millisecond advantage will get them possession of the ball.
For me, this redefines the expertise of an expert athlete. Instead of robotically sculpting movements and beefing up muscles to execute commands in an inactive brain, an expert athlete is comparing and contrasting previous models of him/herself, other athletes, and the environment to make decisions in a fully engaged cerebrum. Simply watch Lionel Messi dribbling by world-class defenders in slow-motion—he’s quite literally playing a different game than the players around him because he’s observing and processing information differently.
The second mechanism we use to find our movement solution is to employ a stockpile of movement commands. These are our baseline actions we issue with each shift in our seat, each turn of the head, and each opening of our mouths. Over time, these commands are altered and perfected to be more nuanced and more complex. This kind of learning is more in line with my idea of an expert athlete who has a large repertoire of easy commands.
To learn better commands isn’t to reduce our error, but to make them faster, more consistent and use less energy—our movements become effortless.
In the first mechanism, we reduce our error over time and we call our error reduction, learning. In the second mechanism, we learn when we improve our speed, accuracy, and energy efficiency in our commands to our limbs.
To learn motor commands and models is to make memories that are necessarily unstable. Memories are strengthened and distorted each time we recall them. There’s even a term in biomechanics called the “forgetting factor” that begins to eat away at your motor abilities just seconds after the action. We can pick up oft-executed actions again after long inactive spans with ease because we’ve built up such a plethora of commands and refined models that remain not as a single memory but a group of memories waiting to be remembered. Somehow we’re able to jump back on the bike, ski down the mountain, or throw a ball after years of inactivity.