Researchers made a surprising finding about IQ and decision-making speed. According to their research, people with higher IQs answer fundamental issues more quickly than people with lower IQs, but they take longer to solve complicated ones.
In 650 subjects’ personalized brain simulations, they found that lower scorers’ brains tended to “jump to conclusions,” whereas higher scorers’ brains took longer but made fewer mistakes. These discoveries significantly impact how we understand how the brain works and may help us cure neurodegenerative illnesses.
Neuroscience News Reported that Researchers from the BIH and Charité—Universitätsmedizin Berlin, along with a colleague from Barcelona, made the unexpected discovery that participants with higher IQ scores were only faster when tackling simple tasks, but that they took longer to solve complex problems than subjects with lower IQ scores.
The 650 participants’ brain simulations allowed the researchers to deduce that brains with less synchrony between brain regions literally “jump to conclusions” rather than delaying decisions until upstream brain regions could finish the processing steps required to solve the problem. The brain models for participants with higher scores required more time to complete complex tasks but made fewer mistakes.
The journal Nature Communications has officially published the researchers’ results. The human brain contains about 100 billion neurons. They each have connections to roughly 1,000 nearby or distant neurons. This incomprehensible network makes the brain’s extraordinary talents possible, which is why it is so challenging to comprehend how the brain functions.
Computer simulations of the human brain are performed by Prof. Petra Ritter, head of the Brain Simulation Section at the Department of Neurology and Experimental Neurology of Charité—Universitätsmedizin Berlin and the Berlin Institute of Health (BIH). She explains the current effort as “wanting to understand how the brain’s decision-making processes work and why different people make different decisions.”
Ritter and her team utilize mathematical models based on a theoretical understanding of biological processes to recreate the workings of the human brain. They also use digital data from MRI and other brain scans. Initially, this yields a “general” model of the human brain. The researchers then improve this model using information from specific individuals, producing “personalized brain models.”
The researchers used data from 650 participants of the Human Connectome Project, a U.S. project examining neural connections in the human brain since September 2010. According to Ritter, the appropriate balance of neurons’ excitement and inhibition “influences decision-making and essentially makes it possible for a person to solve problems.”
Her team knew participants’ IQ scores and performance on rigorous cognitive tests. Ritter states, “We can reproduce the activity of individual brains very effectively.”
“During the process, we discovered that these in silico brains behave differently from one another and similarly to their biological counterparts. Our digital characters are as intelligent and quick-thinking as their biological counterparts. It’s interesting to note that the “slower” brains in both humans and the models were better synced, or in time, with one another. Compared to brains with less synchronization, neuronal circuits in the frontal lobe could delay making decisions for a more extended period.”
The models demonstrated how impaired temporal coordination causes information needed for decision-making to be neither retained in working memory nor readily accessible when needed.
The 650 participants’ brain simulations allowed the researchers to deduce that brains with lower functional connectivity literally “jump to conclusions” rather than delaying decisions until upstream brain regions could finish the necessary processing steps.
Participants were asked to pinpoint the logical rules in a series of patterns. With each challenge, the complexity of these principles grew, making them more challenging to understand. In practical terms, a complex task might entail painstakingly determining the optimal route on a road map, while an easy assignment would involve immediately stopping at a red light.
In the model, many brain groups participating in a choice engage in a so-called winner-take-all competition, with the neural groups for which there is more excellent evidence predominating.
“In more challenging jobs, you must keep track of earlier accomplishments in working memory as you investigate alternative solution avenues and then integrate these into one another. Even though obtaining evidence for a specific solution occasionally takes longer, it produces better outcomes.
We utilized the model to demonstrate how working memory and decision-making at a more granular level of specific neuronal groups are influenced by excitation-inhibition balance at the global level of the entire brain network.
Ritter is happy that the outcomes seen in the digital “brain avatars” align with those of “real” healthy people. After all, she is primarily interested in supporting patients with neurological illnesses like Parkinson’s and dementia.
“The simulation technology utilized in this study has advanced significantly and can enhance personalized in silico planning of medical, pharmaceutical, and therapeutic brain stimulation therapies.
For instance, using a computer simulation, a doctor can already evaluate whether treatment or medication would be most effective for a particular patient and have the fewest adverse effects.
