Efficient Computation of Spreading Activation Using Lazy Evaluation

Spreading activation is an important component of many computational models of declarative long-term memory retrieval but it can be computationally expensive. The computational overhead has led to severe restrictions on its use, especially in real-time cognitive models. In this paper we describe a series of successively more efficient algorithms for spreading activation. The final model uses lazy evaluation to avoid much of the computation normally associated with spreading activation. We evaluate its efficiency on a commonly-used word sense disambiguation task where it is significantly faster than a naive model, achieving an average time of 0.43ms per query for a spread to 300 nodes.