MAC/FAC: A Model of Similarity-Based Retrieval

We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Super cial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical remindings. Our model, called MAC/FAC (for \many are called but few are chosen") consists of two stages. The rst stage (MAC) uses a computationally cheap, non-structural matcher to lter candidates from a pool of memory items. That is, 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 rst stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data.