Proceedings of the Annual Meeting of the Cognitive Science Society

Schema selection involves determining which pre-stored schema best matches the current input.Traditional serial approaches utilize a match/predict cycle which is heavily dependent upon backtracking.This paper presents a parallel interactive model of schema selection called SAMPAN which is more flexible and adaptive. SAMPAN is a hybrid system that combines marker passing with connectionist spreading activation to provide a highly malleable and general representation for schema selection. This work is motivated by recent success in connectionist schema representations and in natural language marker passing systems. A connectionist schema representation provides many attractive features over traditional schema representations. However, a pure connectionist representation lacks generality; new propositions cannot easily be represented. SAMPAN gets around this problem by using marker passing to perform variable binding on generalized concepts. The S A M P A N system is a constraint satisfaction network with nodes that perform simple pattern matching and input summation. This approach is directly applicable to current schema-based systems.