A recently published study challenges the conventional belief that intelligent people think faster. The study discovered that people with higher fluid intelligence, which is a measure of problem-solving ability, actually took more time to solve difficult tasks compared to those with lower fluid intelligence.
The findings, published in Nature Communications, contribute to a better understanding of human intelligence and have potential implications for various fields, including neuroscience, psychology, and artificial intelligence.
The researchers stumbled upon the finding 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 brain areas. Each brain area was represented by excitatory and inhibitory population models, which were based on structural connectomes estimated from brain imaging data.
“My research is focused on brain simulation,” said lead author Michael Schirner, a senior scientist at the Berlin Institute of Health at Charité – Universitätsmedizin Berlin. “I built computational human brain models from MRI data, part of The Virtual Brain project. When working on improving brain models, we found the empirical data about 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 quicker only when the test questions were simple. However, when faced with more challenging tasks that required greater problem-solving, participants with higher intelligence actually took more time to arrive at correct solutions.
“The most surprising insight: since intelligence tests exist (roughly 1890) there was always the assumption that smarter people are smarter because they have faster brains. Turns out: nope!” Schirner remarked.
Previous research suggested that individuals with higher intelligence tend to have faster reaction times. However, this study’s findings challenged that notion by showing that reaction time is not always indicative of intelligence. The researchers proposed a trade-off between decision-making speed and accuracy, which aligns with theories from fields like economy and psychology on fast and slow thinking.
The researchers found that the synchronization between brain regions played a role in problem-solving. A more synchronized brain was better at solving problems, but not necessarily faster. Higher synchronization allowed for better integration of evidence and more robust working memory. This finding was based on the dynamic principles observed in personalized brain network models.
“As synchronization is reduced, decision-making circuits in the brain jump faster to conclusions, while higher synchronization between brain regions allows for better integration of evidence and more robust working memory,” explained Petra Ritter of Charité University, senior author of the study.
“Intuitively this is not so surprising: if you have more time and consider more evidence, you invest more in problem solving and come up with better solutions,” Ritter continued. “Here we not only show this empirically, but we demonstrate how the observed performance differences are a consequence of the dynamic principles in personalized brain network models.”
“What fascinates me is that intelligence is related to the synchrony of the brain, which in turn depends on 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 for understanding the brain’s functioning and to apply this knowledge to the development of bio-inspired tools and robotic applications. The researchers suggested that biologically realistic models may outperform classical artificial intelligence systems in the future.
“It’s now possible to simulate human decision-making in a much more plausible way than, for example, we would imagine intelligence works when looking at ChatGPT,” Schirner said. “There are some crucial differences how biological and artificial intelligence works.”
While the study provides valuable insights into the relationship between intelligence, decision-making speed, and brain network dynamics, it also has some limitations that should be taken into account. The personalized BNMs used in the study are based on simulations and simplifications of the actual human brain. While they provide a useful framework for understanding brain dynamics, they are still abstractions and do not capture the full complexity of the brain’s 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 this direction,” Schirner explained. “There is so much more to do. For example, we need to have 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.
