  Humans have the capacity to question what we see and to recognize when our
vision is unreliable (e.g., when we realize that we are experiencing a visual
illusion). Inspired by this capacity, we present MetaCOG: a hierarchical
probabilistic model that can be attached to a neural object detector to monitor
its outputs and determine their reliability. MetaCOG achieves this by learning
a probabilistic model of the object detector's performance via Bayesian
inference -- i.e., a meta-cognitive representation of the network's propensity
to hallucinate or miss different object categories. Given a set of video frames
processed by an object detector, MetaCOG performs joint inference over the
underlying 3D scene and the detector's performance, grounding inference on a
basic assumption of object permanence. Paired with three neural object
detectors, we show that MetaCOG accurately recovers each detector's performance
parameters and improves the overall system's accuracy. We additionally show
that MetaCOG is robust to varying levels of error in object detector outputs,
showing proof-of-concept for a novel approach to the problem of detecting and
correcting errors in vision systems when ground-truth is not available.
