Learning as the Unsupervised Alignment of Conceptual Systems

To whom correspondence should be addressed; Email: b.roads@ucl.ac.uk. One Sentence Summary: The meaning of concepts resides in relationships across encompassing systems that each provide a window on a shared reality. Abstract Concept induction requires the extraction and naming of concepts from noisy perceptual experience. For supervised approaches, as the number of concepts grows, so does the number of required training examples. Philosophers, psychologists, and computer scientists, have long recognized that children can learn to label objects without being explicitly taught. In a series of computational experiments, we highlight how information in the environment can be used to build and align conceptual systems. Unlike supervised learning, the learning problem becomes easier the more concepts and systems there are to master. The key insight is that each concept has a unique signature within one conceptual system (e.g., images) that is recapitulated in other systems (e.g., text or audio). As predicted, children's early concepts form readily aligned systems. A typical person can correctly recognize and name thousands of objects. However, it remains unclear what mechanism makes this feat possible.