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 .
The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts , and for mapping concepts and their relations across analogs. Building on a recent model of how semantic relations can be learned from non-relational word embeddings, we present a new computational model of mapping between two analogs. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts and of relations between concepts. Through comparisons of model predictions with human performance in a novel mapping task requiring integration of multiple relations, as well as in several classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children. We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations .
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using analogies
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cross-domain analogies
domain-general content
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,
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We
Building on a recent model of how semantic relations can be learned from non-relational word embeddings, we
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analogical mapping , based
mapping between two analogs. The model adopts a Bayesian framework for probabilistic graph matching, operating
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of model predictions
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new analogy experiment with 1, 329 participants,
novel mapping task requiring integration of multiple relations,
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four
several
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The key insight is that
We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to
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, coupled with a generic prior favoring isomorphic mappings, yield human-like analogical mapping