Even intelligent brains take longer to solve difficult problems and they should!

Do you think intelligent people understand things quickly and solve problems easily? This is a general assumption. When someone said this while describing the traits of an  intelligent mind sometime back, this was my reply:

This is not correct, because scientists are considered as intelligent people. But they take years and years to understand a problem. This is because they are the first people to enter a new arena. When something is new and  un-tested before, it takes a  hell of time to understand it and confirm the findings. 

If the knowledge is old, it is easy to  comprehend quickly as already stored data in memory helps . If a problem is unsolved, it takes time for intelligent people to understand it. That is why scientists take so much time to publish papers. It takes years and years of hard work for them to solve problems and understand untried and untested ground.

This is my personal experience. 

Because I cannot understand a problem as soon as I find it. I have to think a lot before understanding it correctly and only after the correct comprehension, can I solve it, also after a lot of thought. only then will be the solution error-free. 

Now new research proved me right. 

Do intelligent people think faster? Researchers at the BIH and Charité—Universitätsmedizin Berlin, together with a colleague from Barcelona, made the surprising finding that participants with higher intelligence scores were only quicker when tackling simple tasks, while they took longer to solve difficult problems than subjects with lower IQ scores.

In personalized brain simulations of the 650 participants, the researchers could determine that brains with reduced synchrony between brain areas literally "jump to conclusions" when making decisions, rather than waiting until upstream brain regions could complete the processing steps needed to solve the problem.

In fact, the brain models for higher score participants also needed more time to solve challenging tasks but made fewer errors. The scientists have now published their findings in the journal Nature Communications.

There are 100 billion or so neurons in the human brain. Each one of them is connected to an estimated 1,000 neighboring or distant neurons. This unfathomable network is the key to the brain's amazing capabilities, but it is also what makes it so difficult to understand how the brain works.

To simulate the mechanisms of the human brain, researchers used digital data from brain scans like magnetic resonance imaging (MRI) as well as mathematical models based on theoretical knowledge about biological processes. This initially resulted in a "general" human brain model. The scientists then refined this model using data from individual people, thus creating "personalized brain models."

For the present study, the scientists worked with data from 650 participants of the Human Connectome Project, a U.S. initiative that has been studying neural connections in the human brain since September 2010. It's the right excitation-inhibition balance of neurons that influences decision-making and more or less enables a person to solve problems. Researchers  knew how participants fared on extensive cognitive tests and what their IQ scores were.

Interestingly, according to this study, the "slower" brains in both the humans and the models were more synchronized, i.e., in time with one other. This greater synchrony allowed neural circuits in the frontal lobe to hold off on decisions longer than brains that were less well coordinated. The models revealed how reduced temporal coordination results in the information required for decision-making neither being available when needed nor stored in working memory.

Gathering evidence takes time—and leads to correct decisions

Resting-state functional MRI scans showed that slower solvers had higher average functional connectivity, or temporal synchrony, between their brain regions. In personalized brain simulations of the 650 participants, the researchers could determine that brains with reduced functional connectivity literally "jump to conclusions" when making decisions, rather than waiting until upstream brain regions could complete the processing steps needed to solve the problem.

Participants were asked to identify logical rules in a series of patterns. These rules became increasingly complex with each task and thus more difficult to decipher. In everyday terms, an easy task would consist of quickly braking at a red light, while a hard task would require methodically working out the best route on a road map. In the model, a so-called winner-take-all competition occurs between different neural groups involved in a decision, with the neural groups for which there is stronger evidence prevailing. Yet in the case of complex decisions, such evidence is often not clear enough for quick decision-making, literally forcing the neural groups to jump to conclusions.

"Synchronization, i.e., the formation of functional networks in the brain, alters the properties of working memory and thus the ability to 'endure' prolonged periods without a decision," explains the lead author of the study who is a neuro-scientist.

In more challenging tasks, you have to store previous progress in working memory while you explore other solution paths and then integrate these into each other. This gathering of evidence for a particular solution may sometimes takes longer, but it also leads to better results. Scientists were able to use the model to show how excitation-inhibition balance at the global level of the whole brain network affects decision-making and working memory at the more granular level of individual neural groups.

Findings are interesting for treatment planning

The results observed in the computer-based "brain avatars" match the results seen in "real" healthy subjects. After all, the main interest is in helping patients affected by neurodegenerative diseases like dementia and Parkinson's disease.

The simulation technology used in this study has made significant strides, and can be used to improve personalized in silico planning of surgical and drug interventions as well as therapeutic brain stimulation. For example, a physician can already use a computer simulation to assess which intervention or drug might work best for a particular patient and would have the fewest side effects.

Now I understand why I take hell of time to come to a conclusion, while others around me can do it so quickly. And I also know why they go wrong so many times, while I rarely make any errors. Some consolation! :)