A recently published study challenges the conventional belief that smart people think faster. The study found that people with higher fluid intelligence, which is a measure of problem-solving ability, actually took longer to solve difficult tasks than those with lower fluid intelligence.
The findings, published in Nature Communicationcontribute to a better understanding of human intelligence and have potential implications in various fields, including neuroscience, psychology and artificial intelligence.
The researchers stumbled upon this discovery while creating personalized brain network models (BNMs) based on data from the Human Connectome project. These BNMs simulated brain activity based on the interaction between different areas of the brain. Each brain area was represented by excitatory and inhibitory population models, based on structural connectomes estimated from brain imaging data.
“My research is focused on brain simulation,” said lead author Michael Schirner, senior researcher at the Berlin Institute of Health at Charité – Universitätsmedizin Berlin. “I built computer models of the human brain from MRI data, part of The Virtual Brain project. By working on improving brain models, we found the empirical data on intelligence.
To compare the brain simulations with real-world data, the researchers analyzed data from 650 participants who took the Penn Matrix Reasoning Test (PMAT). This test consisted of pattern-matching tasks of increasing difficulty, designed to measure fluid intelligence.
Participants with higher intelligence were faster only when the test questions were simple. However, when faced with more difficult tasks requiring greater problem solving, participants with higher intelligence actually took longer to arrive at correct solutions.
“The most surprising idea: Ever since intelligence tests have existed (around 1890), there has always been the assumption that smarter people are smarter because they have faster brains. Turns out: no!” Schirner remarked.
Previous research has suggested that people with higher intelligence tend to have faster reaction times. However, the results of this study challenged this notion by showing that reaction time is not always indicative of intelligence. The researchers proposed a trade-off between speed and accuracy in decision-making that aligns with theories from fields such as economics and psychology about fast and slow thinking.
Researchers have found that synchronization between brain regions plays a role in problem solving. A more synchronized brain solved problems better, but not necessarily faster. Higher synchronization allowed better evidence integration and more robust working memory. This discovery was based on the dynamic principles observed in personalized brain network models.
“As synchronization is reduced, decision-making circuits in the brain reach conclusions more quickly, while higher synchronization between brain regions allows for better integration of evidence and more robust working memory” , explained Petra Ritter of Charité University, lead author of the study.
“Intuitively, it’s not that surprising: if you have more time and consider more evidence, you invest more in problem solving and find better solutions,” Ritter continued. “Here, we not only show this empirically, but demonstrate how the observed performance differences are a consequence of the dynamic principles of personalized brain network models.”
“What fascinates me is that intelligence is related to the synchrony of the brain, which in turn depends on the excitation-inhibition balance,” Schirner told PsyPost.
The study used brain simulation as a complementary tool to observational data to understand how biological networks influence decision-making. The ultimate goal was to develop a theoretical framework to understand how the brain works and to apply this knowledge to the development of bio-inspired tools and robotic applications. The researchers suggested that biologically realistic models could outperform conventional artificial intelligence systems in the future.
“It is now possible to simulate human decision-making in a way that is much more plausible than, for example, we imagine intelligence working when we look at ChatGPT,” Schirner said. “There are crucial differences in how biological and artificial intelligence work.”
While the study provides valuable insights into the relationship between intelligence, decision-making speed, and brain network dynamics, it also has some limitations that need to be considered. The custom BNMs used in the study are based on simulations and simplifications of the real human brain. Although they provide a useful framework for understanding brain dynamics, they remain abstractions and do not capture the full complexity of brain structure and function.
“We would like to build human-level intelligence (artificial general intelligence) by reverse-engineering the brain, and this study was just one step in that direction,” Schirner explained. “There is so much more to do. For example, we need much more detailed brain models, with much more directly learned capabilities.
The study, “Learning How Network Structure Shapes Decision Making for Bio-Inspired Computing,” was authored by Michael Schirner, Gustavo Deco, and Petra Ritter.