Towards metacognitive agents: integrating confidence in sequential decision-making

Overview & Synopsis

In natural cognition, confidence is used to evaluate the quality of decisions and adapt one's behavior to the task at hand. For now, artificial agents lack this kind of metacognitive ability and interact with their environment in a purely reactive way. Inspired by recent findings about the cognitive modeling of confidence, we propose a novel architecture for sequential decision-making. It combines an evidence accumulation model with a metacognitive module that computes and exploits confidence to tune the decision process. The model has been assessed on a perceptual decision-making task, showing promises for more flexible artificial agents and a possible path towards artificial metacognition.