Human brains are constantly processing data to make statistical assessments that translate into the feeling we call confidence, according to a study published in Neuron. This feeling of confidence is central to decision making and, despite ample evidence of human fallibility, the subjective feeling relies on objective calculations. “The feeling ultimately relies on the same statistical computations a computer would make, ” says Adam Kepecs, professor of neuroscience at Cold Spring Harbor Laboratory and lead author of the study.
The development of a model for confidence is a first step towards Kepecs’s ultimate goal to find out where this inner statistician sits in the brain and how it does its data processing.
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The feeling of confidence is often connected to big decisions, such as choosing a career or making financial investments. But confidence also guides decisions about everyday actions, such a making a turn while driving. “Whenever we make decisions, we need confidence, ” says Kepecs. “If we did not have an accurate mechanism for confidence that is usually right, we would have difficulties in correcting decisions or placing bets.”
While human confidence is a feeling, there is also a scientific notion of confidence that relies on statistical methods to calculate the certainty of a hypothesis. Calculating confidence for a statistician involves looking at a set of data — perhaps a sampling of marbles pulled from a bag — and making a conclusion about the entire bag based on that sample. “The feeling of confidence and the objective calculation are related intuitively, ” says Kepecs. “But how much so?”
Previous studies of confidence had largely concluded that the feeling comes from approximations and heuristics, imperfect but useful rules of thumb. In other words, feelings of confidence have some objectivity, but in the end, they are still error-prone shortcuts to the true statistical calculation. “People often focus on the situations where confidence is divorced from reality, ” says Kepecs.
But if confidence were error prone, simple tasks such as deciding to make a turn while driving would be difficult. To determine whether the human feeling of confidence might be an objective calculation, Kepecs created an experiment that had a controlled data stream for individuals to judge. “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, ” says Kepecs.
He and graduate student Joshua Sanders created video games to compare human and computer performance. They had human volunteers listen to streams of clicking sounds and determine which clicks were faster. Participants rated confidence in each choice on a scale of one (a random guess) to five (high confidence). What Kepecs and his colleagues found was that human responses were similar to statistical calculations. The brain produces feelings of confidence that inform decisions the same way statistics pulls patterns out of noisy data.
Kepecs’s model for human confidence stood up to a follow-on experiment in which participants answered questions comparing the populations of various countries. Unlike the perceptual test, this one had the added complexity of each participant’s individual knowledge base.
Even human foibles, such as being overconfident in the face of hard choices with poor data or under-confident when facing easy choices, were consistent with Kepecs’s model. “This subjective feeling of confidence relies on a statistical computation, ” Kepecs says. “Confidence is not a heuristic or a shortcut.”
Kepecs plans to use his model of confidence as a foothold for finding the seat of confidence in the brain and understanding its neural circuitry. “Having a theory about confidence is a required first step to figure out how the brain actually does it, how nerve cells perform this process, ” he says.
The work may also have wider implications. The fields of statistics and, in particular, machine learning, may have something to learn from this inner-statistician. “Humans are still better than computers at solving really difficult problems, ” says Kepecs.
