MAC/FAC: A Model of Similarity-Based Retrieval

摘要

We present a model of similarity-based retrieval that attempts to capture theseseemingly contradictory psychological phenomena: (1) structural commonalities are weighed more heavily than surface commonalities in soundness or similarity judgments (when both members are present); (2) superficial similarity is more important in retrieval from long-term memory than is structural similarity; and yet (3) purely structural (analogical) remindings are sometimes experienced. Our model, called MAC/FAC (for many are called but few are chosen ) consists of two stages. The first stage (MAC) uses a computationally cheap, non-structured matcher to filter candidates from a pool of memory items. We redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented and tested on dozens of examples. We show that MAC/FAC is capable of modeling patterns of access found in psychological data, and illustrative via sensitivity analyses that these results exhibit the desired dependence on theoretically important factors. The relationship of MAC/FAC to other models of memory is discussed, along with implications and possible extensions.