I love a good ruff and tumble with my girl. Tug-of-war is a particular favourite of mine. I know my human likes it too, as she’s constantly shouting: “drop it!” I’m pretty sure that’s a reference to “dropping the mic”. And who doesn’t love a good mic drop? After a long day at work (and my evening walk, of course), my human flops on the sofa and puts her feet up. As every pup knows, this is the universal signal for playtime. Game on! I grab my rope and after some persuading, she gives in and the battle begins. She pulls and then I pull. We are evenly matched, but I refuse to give up. With one last -ditch effort, I pull with all my might. Suddenly I’m on my backside, and I can hear my human giggling in the background. Boy, I did not see that coming.

BUT WHY DID THIS HAPPEN?

Well, it’s all about prediction. Although I can predict what my super strong pulling action will do to the rope, I can’t always predict what my girl will do (I’m not proud of that, so please don’t pass this information along). Before we get to all the ins and outs of this, we need to talk about a few issues first.

The nervous system has a remarkable ability to learn and adapt movement to the changing world around us. To do this, it relies heavily on sensory feedback—what we see, hear and feel—to provide information about our surroundings so that we can move effectively in our environment. However, the information from our senses isn’t perfect. In addition, it takes time for this information to reach the brain and be interpreted. We also have to deal with an environment that is constantly changing, which means that we have to continuously update this information. To compensate for this, the nervous system can use model-based strategies to control how we move.

BUT HOW?

The brain maintains a relationship between our sensory feedback and our motor output (or how we move). Another way of saying this is that the brain maintains an internal model of our sensory and motor systems. You can think of an internal model as a bunch of neurons (i.e., a network) that work together, and which have a general understanding about how the body and environment function. Internal models can predict what sensory information should result from a given movement. It does this by using a copy of the instructions sent to the muscles to perform a movement. It then combines this information with our experience and knowledge about how the body works and feels. You can think of it as though the brain makes educated guesses about what might happen, based on what it’s used to. These predictions are then combined with actual sensory feedback to estimate where the position of the limb and body in space, which is then used to produce subsequent movement. When we perform the same movement enough times, we are able to predict what the outcome might be given that prior experience. For instance, I’m well versed in a game of tug-of-war. I can generally predict when my human is going to pull, and I am able to adjust my grip and movement accordingly. Internal models help us map the relationship between sensory information and our movement, so that we can function effectively in our uncertain environment—or simply defend your tug-of-war winning streak.

BUT WHAT HAPPENS WHEN SOMETHING CHANGES?

As I mentioned before, I consider myself a seasoned pro when it comes to a game of tug-of-war. However, every once in a while, my human will switch things up—like simply letting go in the middle of a game—forcing me to react and/or adapt in these situations. This is also the case for movement in general, where we can experience changes that may affect or impair our performance. These changes can come from the environment or the body itself, such as those that occur with aging or injury. This can cause errors in our movement by changing the normal relationship (or mapping) between sensory input and motor output. When a big enough change happens, there is a mismatch between what we predict and the actual outcome of a movement, and we are less accurate. This is known as a sensory prediction error and is what caused me to land on my backside—to my human’s delight.

I’m well versed in the game of tug-of war, and you can say I have a fair amount of experience. However, after a good rally of back and forth pulling between my girl and I, I wasn’t expecting her to let go. There was a mismatch between what I predicted would happen and what actually happened. I continued to pull with all my might, however no one was pulling back. This resulted in me flying across the room and feeling a little sheepish about my dramatic response.

Fortunately, the brain is awesome at learning from its mistakes, and we are soon able to update our mappings based on our new experiences with changes in the body or environment. Fool me once, shame on you. But fool me twice, shame on me. Turns out she was able to fool me a couple more times, but I eventually caught on and came out victorious. I guess we really can learn from our mistakes.