A study published in the Journal of Experimental Psychology is using magnetic resonance imaging (MRI) to draw the correlation between the attraction toward risk and criminal activity.
Valerie Reyna, PhD, director of both the Human Neuroscience Institute and the Magnetic Resonance Imaging Facility at Cornell University, and her colleagues relied on the theory that non-criminals tend to avoid risk when they’re likely going to win or achieve something, and they pursue risky options when they’re probably going to lose. Yet, based on their findings, criminals demonstrate opposite behavior, and tend to take riskier chances when they’re going to win.
For their study, the researchers used fuzzy-trace based risky-choice framing tasks, a practice that evaluates the relationship between mental representations and decision making, with a mixed group of participants who had both “self-reported criminal or noncriminal tendencies,”according to the Cornell Chronicle. The participants were given two options: take $20 or gamble with a coin flip to win $40 and take nothing. Previous studies show that most people will grab the 20 bucks. However, Dr. Reyna and her researchers found that those who are drawn to criminal activity had a higher instance of selecting the gamble.
To analyze their activity, the researchers examined brain activation through fMRI. They discovered that criminal tendencies were linked to “greater activation in temporal and parietal cortices,” the regions of the brain used for cognitive analysis and reasoning.
“When participants made reverse-framing choices, which is the opposite of what you and I would do, their brain activation correlated or covaried with the score on the self-reported criminal activity,” said Dr. Reyna. “The higher the self-reported criminal behavior, the more activation we saw in the reasoning areas of the brain when they were making these decisions.”
© 2024 Created by radRounds Radiology Network. Powered by
You need to be a member of radRounds Radiology Network to add comments!
Join radRounds Radiology Network