TY - JOUR
T1 - Structural constraints and object similarity in analogical mapping and inference
AU - Krawczyk, Daniél C.
AU - Holyoak, Keith J.
AU - Hummel, John E.
N1 - Funding Information:
Correspondence should be sent to Daniel C. Krawczyk, Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, 3210 Tolman Hall, Berkeley, CA 94720, USA. Email: [email protected] This project was supported by National Science Foundation grant SBR-9729023. We thank Wei-Chung Ooi, and Carla Webster for help running subjects and for the useful feedback. We thank Art Markman for running several SME simulations and for useful comments on an earlier draft, and Dedre Gentner for helpful discussions and comments on an earlier draft.
PY - 2004/2
Y1 - 2004/2
N2 - Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent.
AB - Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent.
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U2 - 10.1080/13546780342000043
DO - 10.1080/13546780342000043
M3 - Review article
AN - SCOPUS:1342284812
SN - 1354-6783
VL - 10
SP - 85
EP - 104
JO - Thinking and Reasoning
JF - Thinking and Reasoning
IS - 1
ER -