From a sports context, think of a baseball batter at the plate trying to hit a fastball. It seems intuitive to watch the ball, time the start of the swing, position the bat at the right height to intercept the ball and send it deep. So, why is hitting a baseball one of the most difficult tasks in sports? Why can’t we perform more consistently?
The problem is noise. Not noise as in the sense of sound but rather the variability of incoming sensory feedback, in other words, what your eyes and ears are telling you. In baseball, the location and speed of the pitch are never exactly the same, so the brain needs a method to adapt to this uncertainty. To do this, we need to make inferences or beliefs about the world.
The secret to this calculation, says Wolpert, is Bayesian decision theory, a gift of 18th century English mathematician and minister, Thomas Bayes. In this framework, a belief is measured between 0, no confidence in the belief at all, and 1, complete trust in the belief. Two sources of information are compared to find the probability of one result given another. In the science of movement, these two sources are data, in the form of sensory input, and knowledge, in the form of prior memories learned from your experiences.
So, our brain is constantly doing Bayesian calculations to compute the probability that the pitch that our eyes tell us is a fastball is actually a fastball based on our prior knowledge. Every hitter knows when this calculation goes wrong when our prior knowledge tells our brain so convincingly that the next pitch will be a fastball, it overrules the real-time sensory input that this is actually a nasty curve ball. The result is either a frozen set of muscles that get no instructions from a confused brain or a swing that is way too early.
Our actions and movements become a never-ending cycle of predictions. Based on the visual stimuli of the approaching baseball, we send a command to our muscles to swing at the pitch at a certain time. We receive instant feedback from our eyes, ears and hands about our success or failure in hitting the ball, then log that experience in our memory.
Wolpert calls this process our “neural simulator” which constantly and subconsciously makes predictions of how our movements will influence our surroundings. “The fundamental idea is you want to plan your movements so as to minimize the negative consequence of the noise,” he explained.
We can get a sense of what its like to break this action-feedback loop. Imagine a pitcher aiming at the catcher’s mitt, releasing the ball but then never being able to see where the pitch ended up. The brain would not be able to store that action as a success or failure and the Bayesian algorithm for future predictions would be incomplete.
Try this experiment with a friend. Pick up a heavy object, like a large book, and hold it underneath with your left hand. If you now use your right hand to lift the book off of your left hand, you’ll notice that your left hand stays steady. However, if your friend lifts the book off of your hand, your brain will not be able to predict exactly when that will happen. Your left hand will rise up just a little after the book is gone, until your brain realizes it no longer needs to compensate for the book’s weight. When your own movement removed the book, your brain was able to cancel out that action and predict with certainty when to adjust your left hand’s support.
“As we go around, we learn about statistics of the world and lay that down,” said Wolpert. “But we also learn about how noisy our own sensory apparatus is and then combine those in a real Bayesian way.”
Our movements, especially in sports, are very complex and the brain to body communication pathways are still being discovered. We’ll rely on self-proclaimed “movement chauvinists” like Daniel Wolpert to continue to map those routes. In the meantime, you can still brag about the pure genius of your five-year-old hitting a baseball.
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