Authors: Marlene Berke, Zhangir Azerbayev, Mario Belledonne, Zenna Tavares, Julian Jara-Ettinger

Abstract: Humans do not unconditionally trust what they see, but instead use their meta-cognition to recognize when a percept might be unreliable or false, such as when we realize that we mistook one object for another. Inspired by this capacity, we propose a formalization of meta-cognition for object detection and we present MetaCOG, an instantiation of this approach. MetaCOG is a probabilistic model that learns, without supervision, a meta-cognition for object detection systems and uses this meta-cognition to refine beliefs about the locations and semantic labels of objects in a scene. We find that MetaCOG can quickly learn an accurate meta-cognitive representation of object detectors and use this meta-cognition to infer the objects in the world responsible for the detections.