When life calls on us to project confidence, whether you're going in for a major job interview, pitching investors or networking at a big conference, the old adage is just to fake it until you make it. But a new study from New York's Cold Spring Harbor Laboratory released this week looks at whether the human feeling of confidence can be broken down into objective mathematical calculation.
Choices that are big, such as launching a new venture, or small, such as deciding when to merge onto the highway, all require a careful weighing of the risks involved. Of course, some of these judgement calls have to occur faster than others.
The study's lead author, associate professor of neuroscience Adam Kepecs, explained the driving question behind the research. "If we can quantify the evidence that informs a person's decision, then we can ask how well a statistical algorithm performs on the same evidence," he says.
To test out the hypothesis, Kepecs and his team developed video games to compare how humans and computers make decisions. The study's human volunteers listened to a series of clicking noises and had to decide which sounds were faster. Then they had to rank their confidence in their choice on a one to five scale, one being a totally random guess and five being high confidence. The next test for the humans was a series of questions about populations around the world.
The research found that human and computer responses were fairly similar. The brain experiences feelings of confidence in the same manner that computers identify patterns in big groups of data. While Kepecs' work helps us better understand how our own minds work, the lessons from this study in others could also prove helpful in developing artificial intelligence.
"Humans are still better than computers at solving really difficult problems," Kepecs says.