Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention

Funding information This work was supported by AFOSRGrant FA-9550-10-1-0177 toMatt Jones. Research in analogical reasoning suggests that higher-order cognitive functions such as abstract reasoning, far transfer, and creativity are founded on recognizing structural similarities among relational systems. Here we integrate theories of analogy with the computational framework of reinforcement learning (RL).We propose a psychology theory that is a computational synergy between analogy and RL, in which analogical comparison provides the RL learning algorithm with ameasure of relational similarity, and RL provides feedback signals that can drive analogical learning. Simulation results support the power of this approach.