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Our model, MAC\/FAC, explains these phenomena in terms of a two\u2010stage process. The first stage uses a computationally cheap, non\u2010structural matcher to filter candidate long\u2010term memory items. It uses content 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\u2010mapping 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 demonstrate 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 alternate model of similarity\u2010based retrieval, and demonstrate that MAC\/FAC explains the data better than ARCS. 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