Brain research shows: Not trying too hard might help us learn quicker

Scott Grafton and colleagues from the University of California, the University of Pennsylvania, and Johns Hopkins University have been studying why some people are able to master a new skill rather quickly while others require more time or practice.

In their study published in Nature Neurosicence researchers measured the connections between different brain regions while participants learned to play a simple game. They discovered that the quickest learners show different neural activity in comparison with the slower ones. The study offers new insight into what happens in the brain during the learning process and sheds light on the role of interactions between different brain regions. Most importantly, the findings suggest that slow learners activate unnecessary parts of the brain for a given task, which is similar to overthinking the problem.

Grafton, a professor in UCSB’s Department of Psychological & Brain Sciences, claimed: “It’s useful to think of your brain as housing a very large toolkit… When you start to learn a challenging new skill, such as playing a musical instrument, your brain uses many different tools in a desperate attempt to produce anything remotely close to music. With time and practice, fewer tools are needed and core motor areas are able to support most of the behaviour. What our laboratory study shows is that beyond a certain amount of practice, some of these cognitive tools might actually be getting in the way of further learning.”

In this study, conducted at UCSB’s Brain Imaging Center, participants played a simple game whilst their brains were scanned with fMRI—the neural activity in their brains were measured by tracking the blood flow in the brain and highlighting which regions are involved in a given task.

The participants responded to a sequence of color-coded notes by pressing the corresponding button on a hand-held controller. Six predetermined sequences of 10 notes each were shown multiple times during the scanning sessions. Participants were instructed to play the sequences as quickly and as accurately as they could, whilst responding to the cues they saw on a screen.

Afterwards, participants practiced the task at home while researchers monitored their activity remotely. Further scans were made at the Brain Imaging Center at two-, four- and six-week intervals to show how efficient the practice was for mastering the skill. The completion time for all participants manifested at different rates. Some subjects picked up the sequences immediately, whilst others gradually improved their skill over the six-week period.

The complexities of learning

Danielle Bassett, lead author and an expert in network science introduced novel analysis methods to determine what was happening in the participants’ brains that correlated with the found differences. As such, the researchers examined the learning process as the function of a complex and dynamic network involving various regions of the brain, rather than trying to find a single active spot in the brain.

The team compared the activation patterns of 112 anatomical regions of the brain and measured the degree to which they mirrored one another. The more the patterns of two regions matched, the more they were considered to be as communicating. By graphing those connections, the team found the hotspots of highly interconnected regions.

Basset, the Skirkanich Assistant Professor of Innovation at the University of Pennsylvania explained: “We weren’t using the traditional fMRI approach where you pick a region of interest and see if it lights up…We looked at the whole brain at once and saw which parts were communicating with each other the most.” He added: “When network scientists look at these graphs, they see what is known as community structure…There are sets of nodes in a network that are really densely interconnected to each other. Everything else is either independent or very loosely connected with only a few lines.”

The strength of brain areas mapped on to the cortical surface. Warm colours indicate high strength in the driver network and cool colours indicate low strength. Credit: Image courtesy of University of California - Santa Barbara

In this study a technique called dynamic community detection was used, a method that employs algorithms to determine which nodes are incorporated into clusters and how their interactions change over time. This allowed the researchers to see how common it was for any two nodes to remain in the same cluster while subjects practiced the same sequence up to 10 times. Through these comparisons researchers found overarching trends and were able to see how regions responsible for different functions worked together.

It was discovered that the visual and the motor blocks had a lot of connectivity during the first few trials, but became essentially autonomous as the experiment progressed. In particular, the part of the brain that controls finger movement and the part that processes visual stimulus didn’t really interact at all by the end of the experiment. This finding was expected by the team as they were looking at the learning process on the neurological level, with the participants’ brains reorganizing the flow of activity while mastering a new skill.

Grafton contemplates, “Previous brain imaging research has mostly looked at skill learning over—at most—a few days of practice, which is silly… Who ever learned to play the violin in an afternoon? By studying the effects of dedicated practice over many weeks, we gain insight into never before observed changes in the brain. These reveal fundamental insights into skill learning that are akin to the kinds of learning we must achieve in the real world.”

Comparing executive function

The research team came up with counterintuitive findings: the participants who showed decreased neural activity learned the fastest. Interestingly, the critical distinction was in cognitive control centres not directly related to seeing the cues or playing the notes: the frontal cortex and the anterior cingulate cortex.

These brain areas are thought to be most responsible for what is known as executive function. Grafton explains: “This neurological trait is associated with making and following through with plans, spotting and avoiding errors and other higher-order types of thinking… In fact, good executive function is necessary for complex tasks but might actually be a hindrance to mastering simple ones.”

Grafton also noted that these cognitive control areas are among the last brain regions to fully develop in humans, which may explain why children are able to acquire new skills quicker than adults.

“It’s the people who can turn off the communication to these parts of their brain the quickest who have the steepest drop-off in their completion times,” claimed Bassett. “It seems like those other parts are getting in the way for the slower learners. It’s almost like they’re trying too hard and overthinking it.”

Further studies of the research team will investigate why some people are better than others at shutting down the connections in these parts of the brains.

The obtained findings might be of outmost importance for most efficient psychotherapeutic practice, especially when much time is spent on psychoeducation and mastering of new skills. It is frequently postulated that control of the frontal cortex is critical for clients’ higher order functioning; however, the outlined research questions this common argument. Will our clients be quicker in their learning processes without too much control and overthinking? Will this kind of learning be of a better quality?

Original source: Abstract for “Neural architecture dynamics during learning” by Danielle S Bassett, Muzhi Yang, Nicholas F Wymbs and Scott T Grafton in Nature Neuroscience. Published online April 6 2015 doi:10.1038/nn.3993.















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