Learning a metacognition for object perception
  Beyond representing the external world, humans also represent their own
cognitive processes. In the context of perception, this metacognition helps us
identify unreliable percepts, such as when we recognize that we are seeing an
illusion. Here we propose MetaGen, a model for the unsupervised learning of
metacognition. In MetaGen, metacognition is expressed as a generative model of
how a perceptual system produces noisy percepts. Using basic principles of how
the world works (such as object permanence, part of infants' core knowledge),
MetaGen jointly infers the objects in the world causing the percepts and a
representation of its own perceptual system. MetaGen can then use this
metacognition to infer which objects are actually present in the world. On
simulated data, we find that MetaGen quickly learns a metacognition and
improves overall accuracy, outperforming models that lack a metacognition.
