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The human brain is the central control organ of our body. It processes information received through the senses and enables us, among other things, to form thoughts, make decisions and store knowledge. Given everything our brain is capable of, it seems almost paradoxical how little we actually still know about it.
Among those who are on the trail of the most complex and complicated organ are Jonas Thiele and Dr. Kirsten Hilger, head of the “Networks of Behavior and Cognition” working group at the Department of Psychology I at the Julius Maximilian University of Würzburg (JMU). Their latest study was recently published in the journal PNAS Nexus: “Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.”
To do this, the researchers used data sets from a large-scale data-sharing project in the USA — the Human Connectome Project. Using fMRI — an imaging method that measures changes in brain activity — over 800 people were examined, both at rest and while they were performing various tasks.
The team led by Würzburg researchers looked at various connections that reflect the strength of communication between brain regions and made predictions about the intelligence of the test subjects based on these observations.
“There are already many such predictive studies and they achieve quite good prediction results,” says Kirsten Hilger. However, the psychologist questions their deeper meaning, since the predictions would never be as accurate as the results of an intelligence test. “We therefore wanted to move away from pure predictions and instead better understand the basic processes in the brain. We hope that this will give us a better understanding of the neural code of individual differences in intelligence.”
Kirsten Hilger hopes that colleagues will follow suit and that more studies will be designed in the future that will improve the conceptual understanding of human cognition with a focus on interpretability.
In its predictions, the team distinguished between three types of intelligence: Fluid intelligence refers to the ability to solve logical problems, recognize patterns and process new information, regardless of existing knowledge or learned skills.
Crystallized intelligence is the knowledge and skills a person acquires throughout his or her life. It includes general knowledge, experiences, and understanding of language and concepts. It is developed through education and experience.
Together, both forms form general intelligence. The best prediction performance was achieved with general intelligence, followed by crystallized and fluid intelligence.
Different theoretical considerations determined which different connections in the brain were examined. In addition, randomly selected connections were also tested. A crucial observation: the distribution of connections across the entire brain and their number were the most important for the prediction performance. More important than the exact brain regions between which the individual connections were located.
“The interchangeability of the selected connections leads us to conclude that intelligence in the brain is a global thing. We were able to predict intelligence not only from a specific set of brain connections, but from different combinations of connections distributed throughout the brain,” says Hilger.
While established theories of intelligence often focus on certain areas of the brain — such as the prefrontal cortex — the results of the study suggest that more connections are responsible for intelligence: “The connections between brain regions that are proposed in the most common theories of neurocognitive intelligence did produce better results than randomly selected connections. But the results were even better when these common connections were expanded to include additional ones,” reports Kirsten Hilger.
This suggests that there are more aspects of intelligence than previously thought that are waiting to be understood.
Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.
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