A recently published study challenges the conventional notion that smart people think fast. The study found that people with high fluid intelligence, a measure of problem-solving ability, actually spent more time solving difficult tasks than those with low fluid intelligence.
The survey results are Nature Communicationscontributes to a deeper understanding of human intelligence, with potential implications for various fields including neuroscience, psychology, and artificial intelligence.
The researchers encountered this finding while creating a personalized brain network model (BNM) based on data from the Human Connectome Project. These BNMs simulated brain activity based on interactions between different brain regions. Each brain region was represented by excitatory and inhibitory population models based on structural connectomes inferred from brain imaging data.
“My research focuses on brain simulation,” said lead author Michael Schilner, a senior researcher at the Berlin Institute for Health Research at Charité University Berlin. “I built a computational model of the human brain from his MRI data. virtual brain project. As we worked to improve brain models, we discovered empirical data on intelligence. ”
To compare the brain simulations with real-world data, the researchers analyzed data from 650 participants who underwent the Penn Matrix Reasoning Test (PMAT). The test consisted of a challenging pattern-matching task designed to measure fluid intelligence.
Participants with higher IQ were faster only when the test questions were simple. However, when faced with more difficult tasks that required more sophisticated problem solving, participants with higher intelligence actually took more time to arrive at the correct solution.
“The most amazing insight is that ever since intelligence tests existed (circa 1890), there has always been the assumption that smart people are smart because they are quick-witted. After all, they aren’t!” said Mr.
Previous research suggests that people with higher intelligence tend to have faster reaction times. However, the results of this study cast doubt on that notion, showing that reaction time does not necessarily indicate intelligence. Researchers have proposed a trade-off between speed and accuracy of decision-making, which is consistent with theories about fast and slow thinking in fields like economics and psychology.
Researchers have found that synchronization between brain regions plays a role in problem solving. A more synchronized brain is better at problem solving, but not necessarily faster. Greater synchronization allowed for better evidence consolidation and a more robust working memory. This finding builds on dynamic principles observed in personalized brain network models.
“With less synchronization, decision-making circuits in the brain reach conclusions sooner, while greater synchronization between brain regions leads to better evidence consolidation and more robust working memory,” he says. The lead author of the study, Petra Ritter of Charité University, explained.
“Intuitively, this shouldn’t be too surprising. The more time we have and the more evidence we consider, the more we will invest in problem solving and come up with better solutions,” Ritter said. continued. “Here, we not only show this empirically, but also demonstrate that the observed performance differences are a result of the dynamic principles of personalized brain network models.”
“What I find interesting is that intelligence has to do with synchrony in the brain, which depends on the balance between excitation and inhibition,” Schilner told Cypost.
In this study, we 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 brain function and apply this knowledge to the development of biologically-inspired tools and robotic applications. The researchers suggested that biologically realistic models could outperform classical artificial intelligence systems in the future.
“For example, it allows us to simulate human decision-making in a much more plausible way than looking at ChatGPT and imagining intelligence at work,” Schilner said. “There are some crucial differences between how biological intelligence and artificial intelligence work.”
Although this study provides valuable insight into the relationship between intelligence, speed of decision-making, and network dynamics in the brain, it also has some limitations to consider. The personalized BNM used in the study is based on simulations and simplifications of the real human brain. While they provide a useful framework for understanding brain dynamics, they remain abstract and do not fully capture the complexity of brain structure and function.
“We want to build human-level intelligence (general artificial intelligence) by reverse-engineering the brain, and this research is just a step in that direction,” Schilner explained. “We still have a lot of work to do. For example, we need more detailed brain models with more direct learning capabilities.”
the study, “Learn how network structure shapes decision-making in bioinspired computing‘ was written by Michael Schirner, Gustavo Deco and Petra Ritter.
