Researchers determine how brain learns scientific concepts
Research coming out of Carnegie Mellon’s Scientific Imaging and Brain Research Center (SIBR) shows that to learn and understand new physics concepts, our brain repurposes ancient structures that are used for everyday purposes such as motion and time intervals.
This concept goes back to the idea that when we learn, we relate old information to new ideas. For example, when envisioning gravity and potential energy, your brain relates the concept to an apple falling from a tree.
When learning periodic circular motion, your brain may associate it with the repeating rhythms of music and dance. This research was carried out by Marcel Just, the D. O. Hebb Professor of Psychology at Carnegie Mellon, and senior research psychologist Robert Mason.
For these experiments, nine right-handed students (three females and six males between the ages of 19 and 25) from physics and engineering departments participated in the study.
They were required to think about common physics concepts in a way that they understood. Researchers then set up a machine learning computer program to identify the concepts’ neural correlates by analyzing brain images. Participants were required to think about the physics concepts six times.
Researchers trained the computer on five sets of those brain images. Then, during the sixth presentation, they asked the computer to identify what physics concept the participant was thinking about from a dataset. The program was able to identify the concept with a high level of accuracy, but that was only half of the researchers’ goal.
They were also curious about which neural properties of the brain allowed the program to identify the concepts.
After examining the results of the experiments, researchers found that humans are able to learn new physics concepts because our brains are programmed to be able to understand four main categories: causal motion, motion with cause and effect relations; periodicity, the repetition of the same motion or phenomenon; energy flow, the transport of energy from one body to another; and algebraic representation, the ability to retrieve a characteristic algebraic equation associated with a scientific variable.
Because all new physics concepts may fall into one of these categories, or at least have some correlation with the four characteristics, our brain is able to encode almost all new physics concepts we learn about.
Moreover, the research shows that each participant’s brain shows the same activation pattern in certain regions when learning the same new concept. That is to say, we learn things in the same way as others do.
“Even though each student may have acquired their physics knowledge in different classrooms or under different methods, [the results] were all predictable when the computer was trained on the data of the students," Mason said.
"This is why humans have been able to move ahead and innovate — because we can use our brain for new purposes.”
According to Just, “Human brains haven’t changed much over a few thousand years, but new fields like aeronautics, genetics, medicine, and computer science have been developed and continuously change.”
This experiment constitutes researchers’ first attempt at finding a neural correlate for abstract concepts that are typically acquired through schooling. This research fits nicely as part of an ongoing project to map out the neural dimensions of semantic knowledge across a variety of domains.
“It will be interesting to determine the nature of brain representations for additional science concepts such as genetics, chemistry or biology," Mason said.
"We hope [these] neural knowledge representations will be useful for education research, potentially for brain-based instruction or evaluation and testing of knowledge using brain-based assessment.”
Furthermore, if we could understands how the brain encodes new scientific concepts, future education would be able to utilize this and provide better teaching and learning experience for education.