A researcher claims to have developed a new mathematical model that can more accurately predict the choices an individual will make.
Romain Ligneul, from the Champalimaud Center for the Unknown in Portugal, believes that the new mathematical model will be able to help refine the insights that will be gleaned from the Iowa Gambling Task.
Predicting Choices In Decision-Making Task
The Iowa Gambling Task, a psychological task developed at the University of Iowa, studies a person's real-life decision-making using cards. Participants are presented with four card decks, each one containing a different mix of cards that can either win or lose them money.
After a while, most people learned that each deck is not created equal. They will keep picking from good decks that will give them valuable cards.
Some, however, will continue drawing cards from the bad deck. This is how researchers detect people who might have problems in the part of the brain that is responsible for decision-making.
Earlier mathematical models have used data from the Iowa Gambling Task to predict a person's card-picking choices. However, Ligneul claims that his mathematical model is more accurate.
He used previous data from about 500 subjects who participated in the Iowa Gambling Task. He discovered that healthy people tend to pick one card from each deck, especially at the beginning of the task. They would cycle through each deck until they learned which one has the valuable cards.
Ligneul incorporated this behavior, which is called sequential exploration, into his new mathematical model.
The study also revealed that as people grow older, their sequential exploration behavior tend to decline. He suggested that this was caused by the neurological changes that occur with age.
"This study provides a mathematical method to disentangle our drive to explore the environment and our drive to exploit it," he stated in a press release. "It appears that the balance of these two drives evolves with aging."
Logneul believes that the new mathematical model, which outperforms previous similar models, can help scientists further understand how various neuropsychiatric conditions, including addiction, can disrupt a person's decision-making and ability to learn.
Details of the new mathematical model were published in the journal PLOS Computational Biology.