AModel of Similarity-based Retrieval

We present a model of similarity-based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (0) structural commonalities are weighed more heavily thon surface commonalities in similarity judgments for items In working memory; (b) in retrieval. superficial similarity is more impor. tont thon structural similarity; and yet (c) purely structural (anologicol) remindings e sometimes experienced. Our model, MAC/FAC. explains th.se phenomena in terms of (I two-stoge process. The first stoge uses (I computationally cheap, nonstructural matcher to filter candidate long-term memory items. It uses conten' vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure-mopping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demon. strate that MAC/FAC can model patterns of access found in psychological data: (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS. an ahernate model of Similarity-based retrieval. and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity.