  The human ability to flexibly reason with cross-domain analogies depends on
mechanisms for identifying relations between concepts and for mapping concepts
and their relations across analogs. We present a new computational model of
analogical mapping, based on semantic relation networks constructed from
distributed representations of individual concepts and of relations between
concepts. Through comparisons with human performance in a new analogy
experiment with 1,329 participants, as well as in four classic studies, we
demonstrate that the model accounts for a broad range of phenomena involving
analogical mapping by both adults and children. The key insight is that rich
semantic representations of individual concepts and relations, coupled with a
generic prior favoring isomorphic mappings, yield human-like analogical
mapping.
