TY - JOUR
T1 - MAC/FAC
T2 - A model of similarity-based retrieval
AU - Forbus, Kenneth D
AU - Gentner, Dedre
AU - Law, Keith
N1 - Funding Information:
This research was supported by the Office of Naval Research (Contract No. NOOO14-89-J-1272). We thank Ray Bareiss, Mark Burstein, Gregg Collins, Ron Ferguson, Brian Falkenhainer, Rob Goldstone, Art Markman, Doug Medin, and Mary Jo Rattermann for discussions of these issues. We thank Paul Thagard for providing us with ARCS and its associated databases. Correspondence and requests for reprints should be sent to Kenneth D. Forbus, Northwestern University, The Institute for the Learning Sciences, 1890 Maple Avenue, Evanston, IL 60201,
PY - 1995
Y1 - 1995
N2 - We present a model of similarity-based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two-stage process. The first stage uses a computationally cheap, non-structural matcher to filter candidate long-term 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-mapping 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-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.
AB - We present a model of similarity-based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two-stage process. The first stage uses a computationally cheap, non-structural matcher to filter candidate long-term 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-mapping 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-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.
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U2 - 10.1016/0364-0213(95)90016-0
DO - 10.1016/0364-0213(95)90016-0
M3 - Article
AN - SCOPUS:58149322672
VL - 19
SP - 141
EP - 205
JO - Cognitive Science
JF - Cognitive Science
SN - 0364-0213
IS - 2
ER -