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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/10928

Title: Toward a computational model of creativity: Novel hypothesis generation from structural knowledge
Authors: Hidaka, Shohei
Keywords: Creativity
Statistical learning
Word learning
Issue Date: 2012-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems (KICSS)
Start page: 175
End page: 180
Abstract: Creativity, generation of a new idea from past experience and knowledge, is one of fundamental aspects of inferential process making progress in many scientific and non-scientific fields. Children's learning at their early development needs to be creative: by nature, they frequently encounter new situations in which they need to infer about things unfamiliar to them. In the present study, we attempt to review empirical and theoretical studies on creative inference in children's word learning. Two theoretical implications for creative cognition are discussed. A computational model of word learning offers a formal way to analyze the relationship between hypothesis generation and structural prior knowledge, which can potentially explain some aspects of empirical findings on new idea generation.
Rights: This is the author's version of the work. Copyright (C) 2012 IEEE. 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems (KICSS), 2012, 175-180. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/10928
Material Type: author
Appears in Collections:a11-1. 会議発表論文 (Conference Papers)

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