Proceedings of the Annual Meeting of the Cognitive Science Society

The relationship between analogy and schema induction is widely acknowledged and constitutes an important motivation for developing computational models of analogical mapping. However, most models of analogical mapping provide no clear basis for supporting schema induction. We describe LISA (Hummel & Holyoak, 1996), a recent model of analog retrieval and mapping that is explicitly designed to provide a platform for schema induction and other forms of inference. LISA represents predicates and their arguments (i.e., objects or propositions) as patterns of activation distributed over units representing semantic primitives. These representations are actively (dynamically) bound into propositions by synchronizing oscillations in their activation: Arguments fire in synchrony with the case roles to which they are bound, and out of synchrony with other case roles and arguments. By activating propositions in LTM, these patterns drive analog retrieval and mapping. This approach to analog retrieval and mapping accounts for numerous findings in human analogical reasoning (Hummel & Holyoak, 1996). Augmented with a capacity for intersection discovery and unsupervised learning, the architecture supports analogical inference and schema induction as a natural consequence. We describe LISA'S account of schema induction and inference, and present some preliminary simulation results.