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Hierarchical Category Structures Facilitate Acquisition of Probabilistic Relational Categories.

, and . CogSci, cognitivesciencesociety.org, (2012)

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Automating the Encoding for the LISA Model of Analogy from Raw Text., and . MAICS, volume 1964 of CEUR Workshop Proceedings, page 115-122. CEUR-WS.org, (2017)Exaggeration of Stimulus Attributes in the Representation of Relational Categories., , and . CogSci, cognitivesciencesociety.org, (2022)Toward a Process Model of Explanation with Implications for the Type-Token Problem., , and . AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence, volume FS-08-06 of AAAI Technical Report, page 79-86. AAAI, (2008)A Solution to the Binding Problem for Compositional Connectionism., , , , , , and . AAAI Technical Report (3), FS-04-03, page 31-34. AAAI Press, (2004)Hierarchical Category Structures Facilitate Acquisition of Probabilistic Relational Categories., and . CogSci, cognitivesciencesociety.org, (2012)A Computational Account of the Development of the Generalization of Shape Information., and . Cogn. Sci., 34 (4): 698-712 (2010)Dynamic Binding in a Neural Network for Shape Recognition, and . Psychological Review, 99 (3): 480--517 (1992)The effects of dual verbal and visual tasks on featural vs. relational category learning., and . CogSci, cognitivesciencesociety.org, (2013)Probing the Mental Representation of Relation-Defined Categories., , and . CogSci, cognitivesciencesociety.org, (2021)Progressive Alignment Facilitates Learning of Deterministic But Not Probabilistic Relational Categories., and . CogSci, cognitivesciencesociety.org, (2011)