Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain - Loued-Khenissi, Leyla; Preuschoff, Kerstin (Frontiers in Artificial Intelligence, 2020)
Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivation. Beyond reward, decisions are guided by belief, i.e. confidence-weighted expectations. Evidence challenging a belief evokes surprise, which signals a deviation from expectation (stimulus-bound surprise) but also provides an information gain. To support the theory that uncertainty minimization is an essential drive for the frugal brain, we probe the neural trace of uncertainty-related decision variables, namely confidence and the dual aspects of surprise, in a discrete decision with a deterministic outcome. Confidence and surprise were elicited with a gambling task administered in a functional magnetic resonance imaging experiment, where agents start with a uniform probability distribution, transition to a non-uniform probabilistic state, and end in a fully certain state. After controlling for reward expectation, we find confidence, taken as the negative entropy of a trial, correlates with a response in the hippocampus and temporal lobe. Stimulus-bound surprise, taken as Shannon information, correlates with an expected response in insula and striatum. In addition, we also find a neural response to information gain in the cerebellum and precuneus, after controlling for reward prediction errors and stimulus-bound surprise. Our results suggest that, even absent an overt need for learning, the human brain expends energy on information gain and uncertainty minimization.