Do you often take chances and yet still land on your feet? Then you probably have a well-developed brain.
This surprising discovery has been made as part of a project studying the brains of young male high and low risk-takers. The tests were carried out at the University of Turku in Finland under the direction of SINTEF, using both the Functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) techniques to measure activation-related and structural correlates of risky behaviour, respectively.
The aim of the project was to investigate the decision-making processes within the brains of 34 young men aged 18 or 19. Based on psychological tests, they were divided into two groups of low and high risk-takers, respectively.
“We expected to find that young men who spend time considering what they are going to do in a given risk situation would have more highly developed neural networks in their brains than those who make quick decisions and take chances,” says SINTEF researcher and behavioural analyst Dagfinn Moe. “This has been well documented in a series of studies, but our project revealed the complete opposite,” he says.
More superhighways among risk-seekers
In fact, images taken of the brains of young men during the study reveal major differences in what is called “white matter.” White matter constitutes the neural network, about 160,000 kilometres in length, that transmits signals in the form of nerve impulses and is crucial to the regulation of internal communication between the different areas of the brain.
This network is designed to analyse and transmit information in a consistent and efficient way. This is why white matter is described as containing the brain’s own “superhighways.” Images from brain scans revealed that those who made quick decisions and took chances during driving simulations had significantly more white matter than those who hesitated, evaluated the situation, and opted to drive safely.
“This finding is interesting and will be important to the way we understand the brain’s development and our learning potential linked to risk-willingness,” says Moe. “This will be useful information for parents, schoolteachers, sports coaches and, not least, driving instructors when it comes to assessing high risk behaviour among young drivers,” he says.
More active, more learning
He believes that the explanation lies in the fact that these young men are active and seek out challenges — both out of curiosity and a hunger to experience learning and a sense of mastery over their environment. This stimulates their brains and so their actions display a fantastic combination of playfulness, seriousness and enjoyment.
“All the positive brain chemicals respond under such conditions, promoting growth factors that contribute to the development of the robust neural networks that form the basis of our physical and mental skills,” says Moe. “The point here is that if you’re going to take risks, you have to have the required skills. And these have to be learned. Sadly, many fail during this learning process — with tragic consequences. So this is why we’re wording our findings with a Darwinian slant — it takes brains to take risks,” he says.
The researchers employed a driving game in which participants were awarded points according to the level of risk they were willing to take.
The 34 young men, aged 18 or 19, were recruited and selected from upper secondary schools in Turku in Finland. The test was laid out in the form of a simulated car journey through 20 sets of traffic lights.
Prior to the tests, the subjects were divided into two groups — high risk-takers (HRT) and low risk-takers (LRT) — on the basis of the psychological sensation-seeking scale developed by Zuckerman, and actual risk-willingness displayed by the participants during initial tests. The game behaviour was the best predictor of risk-taking.
The task assigned to the young men was, on encountering an amber light, to decide whether a) to stop, or b) to take a chance, run the light and complete the journey through all 20 traffic lights as quickly as possible. A decision to stop added three seconds to the time taken, and a collision six seconds. In other words, the best times would be achieved by those successfully running amber lights and avoiding collisions — but you wouldn’t know if you were going to encounter another car on the crossings.
All the participants tried out the game before they started the formal tests, when they were subject to an MR scan of their brains. Prior to the tests they were all assessed for and cleared of any anatomical deficiencies or mental health problems or conditions that might have influenced the cognitive functions that were going to be measured. They were all right-handed.
The first measurement, performed with fMRI, analysed local activation differences in the gray matter of the brain between experimental conditions. FMRI registers changes in blood oxygenation and flow occurring as a result of changes in neuronal activity. The second measurement involved a Diffusion Tensor Imaging (DTI) analysis to estimate between-group difference in white matter integrity depending particularly on the quality of the myelin sheath enclosing the nerve fibres. Myelination of neural fibers is an indicator of brain maturation related to increasing efficiency of impulse transmission. The results thus provide a picture of local neural activity at the moments when decisions are taken by individuals in the two groups, as well as between-group structural difference in the quality of the brain’s signal transmission system.
How do risk-takers think?
Measurements of the moment that decision-making actually takes place are taken when the subject chooses to press either “stop” or “go.”
Results showed that high risk-seekers didn’t hesitate for long before they made their decisions. Their optimism, willingness to take a chance, and belief that they would win determined their decision. Low risk-seekers, on the other hand, found themselves in a dilemma. Should they take a chance? What would happen if they crashed? This resulted in them hesitating before they made a decision to run the amber light by pressing the “go” button. Choosing the “stop” button is the safe decision that resulted in no dilemma.
Analysis of the white matter in the two groups also revealed major differences.
Local differences in white matter are evident between high and low risk-takers as illustrated by the coloured areas adjacent to the prefrontal cortex, within interhemispheric tracts, and in the rear of the brain that controls vision.
“Daring and risk-willingness activate and challenge the brain’s capacity and contribute towards learning, coping strategies and development,” says Moe. “They can stimulate behaviour in the direction of higher levels of risk-taking in people already predisposed to adapt to cope optimally in such situations. “We must stop regarding daring and risk-willingness simply as undesirable and uncontrolled behaviour patterns,” he says.
Together with the Centre for Cognitive Neuroscience at the University of Turku, Moe is currently planning a new study to investigate educational approaches directed towards both high and low risk-seekers.
“This project will be incorporated within the ‘Mind, Brain and Education (MBE)’ concept, in which knowledge about the brain is more closely integrated into our understanding of educational methods and teaching outcomes,” he says.
“We believe that this result is a very important contribution towards our understanding of how important factors such as curiosity, daring and play are for the development of the brain, as well as our physical and mental skills,” he says, referring to Fridtjof Nansen’s characterisation of the phenomenon: ‘A spirit of daring is deeply ingrained in our nature — in each and every one of us. But accidents will befall those who are unprepared’.
This article originally appeared at SINTEF.
- Victor Vorobyev, Myoung Soo Kwon, Dagfinn Moe, Riitta Parkkola, Heikki Hämäläinen. Risk-Taking Behavior in a Computerized Driving Task: Brain Activation Correlates of Decision-Making, Outcome, and Peer Influence in Male Adolescents. PLOS ONE, 2015; 10 (6): e0129516 DOI: 10.1371/journal.pone.0129516