“Zoning Out” Actually Helps You Learn? Data From Up To 90,000 Brain Cells Says So

Ever get told off in school for letting your mind wander? It’s time to feel vindicated. According to a new study, “zoning out” could actually be helping us to learn, so if you want to pause here and share this article with all your old teachers, we’ll understand.

Done? Okay, let’s get into it. Scientists at the Howard Hughes Medical Institute (HHMI) took 89 separate recordings of neuronal activity from a population of 19 specially bred mice. That culminated in data from tens of thousands of neurons, which suggested that even when we’re not engaged with a specific task or goal, our brains can still be learning valuable information.

“Theorists have long postulated for decades that the brain might be using unsupervised learning,” first author and research scientist at HHMI, Lin Zhong, told IFLScience. “Humans definitely employ unsupervised learning, a powerful learning skill everyone [is] born with.”

Say you’re at the mall, but you’re not shopping for anything specific – you’re just aimlessly wandering, taking a glance into store windows and not following any particular route. You might feel like your brain is “switched off”, but it’s actually still paying really close attention to your surroundings, for a very important reason.

“Even when you are zoning out or just walking around or you don't think you are doing anything special or hard, your brain is probably still working hard to help you memorize where you are, organizing the world around you, so that when you're not zoning out anymore – when you actually need to do something and pay attention – you're ready to do your best,” explained group leader Marius Pachitariu in a statement. 

For their mouse experiments, the team set up virtual reality environments (yes, they do make VR headsets for mice, and for cows too in case you were wondering). The environments featured corridors with a variety of textures to mirror different real-world environments, and some of the textures were associated with rewards while others were not.

The mice were let loose in their new virtual worlds for several weeks while the wealth of neuronal recordings were taken. As they learned the rules and reward system, the experimenters could make tweaks to keep things fresh. But when analyzing the data, visualizing the cortex using a tool developed in-house, the team were coming up against some findings they couldn’t explain. 

The animals were displaying evidence of neural plasticity in the visual cortex. This is the brain’s incredible ability to form and reform new neuronal connections over the course of a lifetime in response to the information with which it’s constantly being bombarded. 

In the case of the mice, the plasticity they were showing didn’t seem to be explained by the task they were learning.

“As we thought more and more about it, we eventually ended up on the question of whether the task itself was even necessary. It’s entirely possible that a lot of the plasticity happens just basically with the animal’s own exploration of the environment,” said Pachitariu.

Given that unsupervised learning happens all the time, we learn more from just observing people around us, such as family members and friends, than from being taught by them.

Lin Zhong

In fact, they found that mice left to explore the virtual corridors for weeks cottoned on to the rewards system much faster than mice that had undergone formal training on the task.

It demonstrates what many intuitively know to be true: that you don’t always need to be “taught” something in order to learn.

“Given that unsupervised learning happens all the time, we learn more from just observing people around us, such as family members and friends, than from being taught by them,” Zhong told IFLScience.

The study suggests that we may need to give unsupervised learning another look because, as Pachitariu said, “if that’s the main way by which the brain learns, as opposed to a more instructed, goal-directed way, then we need to study that part as well.”

We asked Zhong what impact these insights could have on education. “I would focus on the environments in which students are learning,” Zhong told us, “since it provides the data (environments) for us to learn. For example, a kid tends to grow up to be respectful when surrounded by positive role models, and vice versa.”

And there are other potential applications. “Our work paves the way for understanding the differences and similarities between artificial and biological intelligence,” added Zhong. “Further studies on how different learning algorithms in the brain are implemented and how they interact with each other in different learning contexts could allow a better understanding of how the brain learns.”

“I think this will bring the fields of artificial and biological intelligence closer together, future advanced AIs could potentially benefit from it.”

Not bad, considering we began this tale by reminding you of all the times you used to drift off in math class.

The study is published in Nature.