Many educators, from elementary school teachers to tenured law school professors, believe the key to learning and education is repetition. Continue to do something, or study something, long enough and at least some of that information is bound to sink in eventually, right?
While repetition may not be completely devoid of educational merit, a new neurological study just released by the University of Pittsburgh and Carnegie Mellon University finds engagement reigns supreme when it comes to learning.
When it comes to learning anything, whether that be the periodic table of elements for a chemistry test or how to play Stairway to Heaven on guitar, many factors influence how much is actually grasped and retained. Motivation, cognitive arousal, general attention, and engagement, just to name a few. The research team behind these findings set out to investigate which of these “internal states” influence learning outcomes the most.
“Intuitively, we know that neural activity changes as we’re learning different things, because our behavior gets better with practice,” explains Jay Hennig, a graduate student in neural computation and machine learning at Carnegie Mellon University. “However, what we’re finding is that it’s not just about getting better. All of the things that go on alongside learning, such as one’s level of attention or state of arousal, play a significant role.”
This was made possible on a neurological level via brain-computer interface (BCI) technology. In other words, as monkeys played a computer game, researchers tracked their brain activity and pupil dilations. The game was simple, consisting of various tasks focusing on moving a cursor around.
As the research progressed, study authors started noticing large, periodic, and unexpected neural fluctuations within the motor cortex of some study subjects. Initially, researchers were surprised by these aberrations and unsure of their origin. That is until they realized that the fluctuations only took place whenever a participant was particularly surprised by something that happened in the computer game.
Whenever this occurred, participants’ eyes would also dilate, indicating that those neural aberrations researchers saw were the “neural manifestations” of greater engagement. This also makes sense on a purely practical level; whenever something unexpected happens in life, we’re fully engaged in whatever just happened at that moment.
During the actual round of the surprise or unexpected event, none of the participating primates who showed greater engagement performed any better than other participants. However, by the time of the next game, subjects who displayed neural fluctuations consistent with greater engagement learned how to succeed in the game faster than the others.
“We weren’t looking for this particular effect in the neural data,” says Steve Chase, an associate professor of biomedical engineering at Carnegie Mellon and the Neuroscience Institute. “The pupil diameter was tightly correlated with the engagement signal that we saw in the neural activity, and it seems to have a massive effect in the motor cortex.”
Study authors say these results indicate engagement & attention levels outclass other factors influencing learning efficiency like motivation or repetition.
So, memorizing notes from a blackboard by reading them over and over may help you pass the test, but greater engagement in that content will lead to a more well-rounded understanding of the subject. The same notion applies to learning how to skateboard or ride a bike; repetition will get the job done eventually but you’ll pick up the skill much faster if you truly engage with the task and give it your full attention.
“You might have imagined that the brain would be set up with a clear segregation of functions, like motor areas to motor control, and emotional areas to emotional control, and sensory areas to sensory representation,” explains Aaron Batista, a professor of bioengineering at the University of Pittsburgh. “What we’re finding is a serendipitous kind of intrusion of an internal state into a motor area. It could be that we can harness that signal to improve learning.”
“The findings here might one day help people learn everyday skills, such as math or dance, more quickly and to a higher level of proficiency,” concludes Byron Yu, professor of biomedical engineering and electrical and computer engineering at Carnegie Mellon.