May 18, 2024

Brain Speed vs Intelligence: New Study Disputes Long-Held Beliefs

Ritters laboratory and lots of other research groups at HBP usage brain simulation to enhance observational information, in order to establish a theoretical framework of how the brain works.
In this case, brain simulation has actually been utilized to identify the link in between structural and practical connection in the brain and cognitive efficiency. “As synchronization is decreased, decision-making circuits in the brain dive quicker to conclusions, while greater synchronization in between brain regions allows for better combination of proof and more robust working memory,” says Ritter. The simulations were run utilizing a multi-scale brain modeling method; brain imaging information were processed with automated containerized pipelines. The processing of the extremely delicate brain information took place within a safe Virtual Research Environment of EBRAINS Health Data Cloud.

A brand-new study refutes the idea that intelligence relates to quicker thinking, revealing rather that individuals with greater fluid intelligence take more time on complex problems due to their brains integrated activity permitting much deeper evidence factor to consider and analytical. Credit: Petra Ritter
Do intelligent individuals believe faster than others when solving issues? New findings by researchers from the Human Brain Project at Charité University Berlin and their partner at University Pompeu Fabra in Barcelona question this deeply ingrained belief in the field of intelligence research.
The outcomes of their research study were recently released in the journal Nature Communications.
Taking a biologically inspired approach, they built 650 customized brain network models (BNMs). These were created using data collected from the Human Connectome Project and made it possible for the team to simulate the processes the brain undergoes throughout analytical.

Observations from the brain simulations were compared to empirical data of the 650 individuals taking the so-called Penn Matrix Reasoning Test (PMAT), consisting of a series of progressively hard pattern-matching tasks. The results of these were quantified into participants fluid intelligence (FI), which might approximately be referred to as the ability to take challenging choices in brand-new scenarios.
” We found that individuals scoring higher on fluid intelligence (FI) took more time to solve the more challenging tasks compared to individuals with lower FI. They were just quicker when responding to easy concerns,” describes Petra Ritter of Charité University, senior author of the study. “We initially observed this in our simulations, and after that just afterward we saw that the empirical data of individuals taking the intelligence tests represented this pattern.” Ritters lab and many other research study groups at HBP use brain simulation to enhance observational data, in order to develop a theoretical framework of how the brain works.
In this case, brain simulation has been utilized to figure out the link in between functional and structural connection in the brain and cognitive efficiency. A more synchronized brain is better at resolving problems, but not always faster. “As synchronization is decreased, decision-making circuits in the brain dive quicker to conclusions, while greater synchronization between brain areas permits for better combination of proof and more robust working memory,” states Ritter. “Intuitively this is not so surprising: if you have more time and think about more evidence, you invest more in problem-solving and create better solutions. Here we not only reveal this empirically, but we show how the observed performance distinctions are a repercussion of the vibrant concepts in customized brain network models. We therefore present new evidence that challenges a common concept about human intelligence.”
Formerly established regional circuit models of working memory (WM) and decision-making (DM), both crucial for intelligence, were plugged into The Virtual Brain (TVB), of which the latter provided a simulation at the whole-brain level.
The simulations were run utilizing a multi-scale brain modeling approach; brain imaging information were processed with automatic containerized pipelines. The processing of the highly delicate brain information happened within a safe Virtual Research Environment of EBRAINS Health Data Cloud. These innovations are available through EBRAINS to the international research study community.
The supreme objective of the study is not to discover how fast you need to believe but rather to understand how biological networks figure out decision-making for the advancement of bio-inspired tools and robotic applications. Designing brain characteristics of smart decision-making is for that reason an appealing technique to building clever applications. “We think that biologically more reasonable models might outshine classical A.I. in the future,” states Ritter.
Referral: “Learning how network structure shapes decision-making for bio-inspired computing” by Michael Schirner, Gustavo Deco, and Petra Ritter, 23 May 2023, Nature Communications.DOI: 10.1038/ s41467-023-38626-y.