To understand how the brain works, researchers typically present study subjects in a brain scanner with simple stimuli, like a number against a black background, to see which regions of the brain respond. The results help them gain a better understanding of what part of the brain is responsible for learning skills, like math or reading.
But it’s not always clear how applicable these neural patterns, generated in an isolated experiment, are to how the brain works in the real world—such as a classroom.
In a new study, Jessica Cantlon, a cognitive scientist at the University of Rochester and her colleagues used functional magnetic resonance imaging (fMRI) to look at brains of children during a normal educational activity—watching Sesame Street—to get a better of picture of how the brain changes as it develops reading and math skills. “It is not currently possible to measure the real-world thought process that a child has while observing an actual school session. However, if it could be done, children’s neural processes would presumably be predictive of what they know,” the authors write in the study, published in the journal PLoS Biology.
“Everyone would prefer to use the real world as the stimulus because that’s really the goal: to understand what brain regions are important when children are learning in the real classroom and not with isolated stimuli on a black background, because that’s not really how they learn,” says Cantlon, whose research team is making strides in understanding brain development in everyday settings.
By using fMRIs, which provide real-time information about brain activity, while the children watched an episode of Sesame Street, the researchers argue they can examine a child’s neural processes as they are engaged naturally, putting them one step closer to understanding the complex way that different environmental influences and stimuli cooperate in the learning process. Eventually, such insight could lead to more accurate diagnosis and treatment of learning disabilities.
In the study, Cantlon and her team placed 27 kids between the ages 4 and 11, and 20 adults in the fMRI machine as the participants watched 20 minutes of Sesame Street, which featured short clips on numbers, shapes and language. After the episode, the kids took standardized tests that assessed their math and verbal abilities. Using the fMRI scans, the researchers created neural maps of the kids’ thought processes and compared those maps to the patterns found among the adult participants.
They found that kids whose brains worked in similar ways to that of the adults received higher scores on their standardized tests. This suggests that the brain develops along a predictable pattern as we age, dedicating certain regions and networks to specific tasks, such as reading, or problem solving. The same brain images also revealed where verbal and math skills tend to develop.
Kids with neural patterns similar to those of adults in the Broca’s area of the brain, which is associated with speech and language, for example, scored higher in verbal skills. Children with neural maturity in the intraparietal sulcus (IPS) region of the brain, which is involved with number processing, had better math scores.
Then, in order to document how generalizable lab-based findings of brain regions are, Cantlon and her team asked the same participants who viewed the Sesame Street episode to take part in a more traditional fMRI experiment in which they were asked to match shapes, numbers and faces while their brains were scanned. Afterward, the children took standardized reading and math tests. But this time, when the researchers compared the scans to the reading and math scores, they found that the brain patterns did not predict test scores as they did in the more naturalistic setting of watching the Sesame Street clip. They concluded that monitoring kids’ neural activity during the educational Sesame Street show better revealed the unconstrained and spontaneous thought processes that are essential to learning, and therefore was a better predictor of math and verbal performance.
Using brain imaging to study naturalistic thought is gaining more traction in the scientific community, since people generally absorb information from complex situations with multiple stimuli, such as while attending class, interacting with family at home, watching TV or using the computer. Simple and isolated experiments in a lab may fall short of replicating the complexity of these learning environments.
By studying how the brain engages in a more natural learning setting, the researchers also hope to document the varying degrees to which different regions of the brain are involved in specific learning tasks. That knowledge could ultimately lead to using brain scans to diagnose and assess learning disabilities in kids.
“There are good cognitive and behavioral tests for understanding what’s specifically wrong with a child if they have a math learning impairment, but having this brain data provides another independent source of data that you could then use to understand the nature of that problem to say whether it is a working memory problem, or it is a number-specific problem,” says Cantlon. “If you know what patterns of brain activity represent and differentiate those types of problems that could contribute to mathematics impairment, then you could tailor an intervention a little bit more specially to their problem.” Which would certainly be welcome by the nearly 8% of young children who struggle with learning disabilities in the U.S.