Optimal policy for uncertainty estimation concurrent with decision making

Abstract

Decision making often depends on vague information that leads to uncertainty, which is a quantity contingent not on choice but on probability distributions of sensory evidence and other cognitive variables. Uncertainty may be computed in parallel and interact with decision making. Here, we adapt the classic random-dot motion direction discrimination task to allow subjects to indicate their uncertainty without having to form a decision first. The subjects' choices and reaction times for perceptual decisions and uncertainty responses are measured, respectively. We then build a value-based model in which decisions are based on optimizing value computed from a drift-diffusion process. The model accounts for key features of subjects' behavior and the variation across the individuals. It explains how the addition of the uncertainty option affects perceptual decision making. Our work establishes a value-based theoretical framework for studying uncertainty and perceptual decisions that can be readily applied in future investigations of the underlying neural mechanism.