Researchers report that they can predict "with unprecedented accuracy" how well you will do on a complex task such as a strategic video game simply by analyzing activity in a specific region of your brain.The findings, published in the online journal PLoS ONE, offer detailed insights into the brain structures that facilitate learning, and may lead to the development of training strategies tailored to individual strengths and weaknesses.
The new approach used established brain imaging techniques in a new way. Instead of measuring how brain activity differs before and after subjects learn a complex task, the researchers analyzed background activity in the basal ganglia, a group of brain structures known to be important for procedural learning, coordinated movement and feelings of reward.
Using magnetic resonance imaging and a method known as multivoxel pattern analysis, the researchers found significant differences in patterns of a particular type of MRI signal, called T2*, in the basal ganglia of study subjects. These differences enabled researchers to predict between 55 and 68 percent of the variance (differences in performance) among the 34 people who later learned to play the game.
"There are many, many studies, hundreds perhaps, in which psychometricians, people who do the quantitative analysis of learning, try to predict from SATs, GREs, MCATS or other tests how well you're going to succeed at something," said University of Illinois psychology professor and Beckman Institute director Art Kramer, who led the research. These methods, along with studies that look at the relative size of specific-brain structures, have had some success predicting learning, Kramer said, "but never to this degree in a task that is so complex."
"We take a fresh look at MRI images that are recorded routinely to investigate brain function," said Ohio State University psychology professor Dirk Bernhardt-Walther, who designed and performed the computational analysis together with Illinois electrical and computer engineering graduate student Loan Vo. "By analyzing these images in a new way, we find variations among participants in the patterns of brain activity in their basal ganglia," Bernhardt-Walther said. "Powerful statistical algorithms allow us to connect these patterns to individual learning success. Our method may be useful for predicting differences in abilities of individuals in other contexts as well," he said. "Testing this would be inexpensive because the method recycles MRI images that are recorded in many studies anyway."
The new approach used established brain imaging techniques in a new way. Instead of measuring how brain activity differs before and after subjects learn a complex task, the researchers analyzed background activity in the basal ganglia, a group of brain structures known to be important for procedural learning, coordinated movement and feelings of reward.
Using magnetic resonance imaging and a method known as multivoxel pattern analysis, the researchers found significant differences in patterns of a particular type of MRI signal, called T2*, in the basal ganglia of study subjects. These differences enabled researchers to predict between 55 and 68 percent of the variance (differences in performance) among the 34 people who later learned to play the game.
"There are many, many studies, hundreds perhaps, in which psychometricians, people who do the quantitative analysis of learning, try to predict from SATs, GREs, MCATS or other tests how well you're going to succeed at something," said University of Illinois psychology professor and Beckman Institute director Art Kramer, who led the research. These methods, along with studies that look at the relative size of specific-brain structures, have had some success predicting learning, Kramer said, "but never to this degree in a task that is so complex."
"We take a fresh look at MRI images that are recorded routinely to investigate brain function," said Ohio State University psychology professor Dirk Bernhardt-Walther, who designed and performed the computational analysis together with Illinois electrical and computer engineering graduate student Loan Vo. "By analyzing these images in a new way, we find variations among participants in the patterns of brain activity in their basal ganglia," Bernhardt-Walther said. "Powerful statistical algorithms allow us to connect these patterns to individual learning success. Our method may be useful for predicting differences in abilities of individuals in other contexts as well," he said. "Testing this would be inexpensive because the method recycles MRI images that are recorded in many studies anyway."
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