A Theory of Relation Learning and Cross-domain Generalization

Understand

People readily generalize knowledge to novel domains and stimuli.

• We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations.
• The model is an extension of the LISA and DORA models of relational inference and learning.
• The resulting model learns both the content and format (i.e., structure) of relational representations from non-relational inputs without supervision, when augmented with the capacity for reinforcement learning, leverages these representations to learn individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference.