  A key competence for open-ended learning is the formation of increasingly
abstract representations useful for driving complex behavior. Abstract
representations ignore specific details and facilitate generalization. Here we
consider the learning of abstract representations in a multi-modal setting with
two or more input modalities. We treat the problem as a lossy compression
problem and show that generic lossy compression of multimodal sensory input
naturally extracts abstract representations that tend to strip away modalitiy
specific details and preferentially retain information that is shared across
the different modalities. Furthermore, we propose an architecture to learn
abstract representations by identifying and retaining only the information that
is shared across multiple modalities while discarding any modality specific
information.
