Bayesian inference in motor learning can help us get better at doing things like riding a bike or hitting a baseball. When we learn a new skill, our brain tries to figure out the best way to do it by making predictions and testing them out.
Imagine you're riding a bike for the first time. Your brain makes an initial prediction about how to balance and pedal, but it's probably not perfect. As you keep trying, your brain adjusts its prediction based on what's working and what's not.
Bayesian inference helps us figure out which adjustments to make. It does this by considering both the data we're receiving (like the feel of the bike and how far we traveled) and our prior knowledge (like what we've learned about balance and pedaling).
For example, if we feel the bike tipping to one side, our brain might use Bayesian inference to update its prediction about how to adjust our balance. It may also consider our prior knowledge about how bikes work to make the most likely adjustment.
Overall, Bayesian inference helps our brains better understand how to perform motor skills by combining new data with what we already know. This can lead to more efficient and effective learning.