An Emergent Approach to Analogical Inference

Title:
An Emergent Approach to Analogical Inference
Authors:
Thibodeau, Paul; Flusberg, Stephen J.; Glick, Jeremy J.; Sternberg, Daniel A.
Abstract:
In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.
Citation:
Paul H. Thibodeau, Stephen J. Flusberg, Jeremy J. Glick & Daniel A. Sternberg (2013) An emergent approach to analogical inference, Connection Science, 25:1, 27-53.
Publisher:
Taylor & Francis
DATE ISSUED:
2013-08-13
Department:
Psychology
Type:
article
PUBLISHED VERSION:
10.1080/09540091.2013.821458
PERMANENT LINK:
http://hdl.handle.net/11282/309706

Full metadata record

DC FieldValue Language
dc.contributor.authorThibodeau, Paulen_US
dc.contributor.authorFlusberg, Stephen J.en_US
dc.contributor.authorGlick, Jeremy J.en_US
dc.contributor.authorSternberg, Daniel A.en_US
dc.date.accessioned2013-12-23T16:15:56Z-
dc.date.available2013-12-23T16:15:56Z-
dc.date.issued2013-08-13en
dc.identifier.citationPaul H. Thibodeau, Stephen J. Flusberg, Jeremy J. Glick & Daniel A. Sternberg (2013) An emergent approach to analogical inference, Connection Science, 25:1, 27-53.en_US
dc.identifier.issn0954-0091en_US
dc.identifier.urihttp://hdl.handle.net/11282/309706-
dc.description.abstractIn recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.en_US
dc.language.isoen_USen_US
dc.publisherTaylor & Francisen_US
dc.identifier.doi10.1080/09540091.2013.821458-
dc.subject.departmentPsychologyen_US
dc.titleAn Emergent Approach to Analogical Inferenceen_US
dc.typearticleen_US
dc.identifier.journalConnection Scienceen_US
dc.subject.keywordAnalogyen_US
dc.subject.keywordInferenceen_US
dc.subject.keywordRelational reasoningen_US
dc.subject.keywordDevelopmenten_US
dc.subject.keywordConnectionismen_US
dc.subject.keywordNeural networken_US
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