TY - GEN
T1 - Learning Relational Concepts through Unitary versus Compositional Representations
AU - Corral, Daniel
AU - Jones, Matt
N1 - Publisher Copyright:
© CogSci 2017.
PY - 2017
Y1 - 2017
N2 - Current theories of relational learning on structure mapping emphasize the importance of compositional representations, based on the concept's components and the relations among them. We consider the possibility that relational concepts can also be represented unitarily, whereby the concept is a property of the stimulus as a whole. The distinction between compositional and unitary representations of relational concepts is a natural consequence of structure-mapping theory, but its psychological implications have not been explored. We report two experiments in which we examine how encouraging subjects to represent relational concepts compositionally versus unitarily affects learning on classification- and inference-based category learning tasks. Our findings show that unitary representations lead to better learning than compositional representations, especially for the inference task. We conclude that unitary representations incur less cognitive load than structural alignment of compositional representations, and thus may be the default for everyday relational reasoning.
AB - Current theories of relational learning on structure mapping emphasize the importance of compositional representations, based on the concept's components and the relations among them. We consider the possibility that relational concepts can also be represented unitarily, whereby the concept is a property of the stimulus as a whole. The distinction between compositional and unitary representations of relational concepts is a natural consequence of structure-mapping theory, but its psychological implications have not been explored. We report two experiments in which we examine how encouraging subjects to represent relational concepts compositionally versus unitarily affects learning on classification- and inference-based category learning tasks. Our findings show that unitary representations lead to better learning than compositional representations, especially for the inference task. We conclude that unitary representations incur less cognitive load than structural alignment of compositional representations, and thus may be the default for everyday relational reasoning.
KW - Category Learning
KW - Concept Representation
KW - Inference
KW - Relational Learning
KW - Relational Structure
UR - http://www.scopus.com/inward/record.url?scp=85055668376&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85055668376
T3 - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition
SP - 1830
EP - 1835
BT - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
T2 - 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Y2 - 26 July 2017 through 29 July 2017
ER -