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Identifying Creativity During Problem Solving Using Linguistic Features

机译:使用语言特征解决问题时识别创造力

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摘要

Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants' language while completing collaborative divergent thinking tasks. To this end, 78 participants' conversational dialogs (i.e., 39 dyads) within a chat environment were collected while completing three open-ended problem-solving tasks. Expert raters scored the dialogs in terms of fluency, flexibility, elaboration, and originality, as well as three types of creative language (metaphor and simile, humor, and word play). Factor analyses indicated that these scores captured two main constructs (creativity and elaboration). The linguistic features of the participants' language (captured computationally using natural language processing tools) accounted for significant amounts of variation in both the creativity (R-2=.640) and elaboration (R-2=.550) scores within linear mixed effect (LME) models. These results highlight specific linguistic features that can be used to explain large amounts of variance in constructs related to creativity.
机译:通常使用不同的思维任务进行评估,这些任务通常会评估参与者输出的流畅性,灵活性,原创性和阐述各种不同任务的流畅性,灵活性,创造性。本研究评估了创造力可以根据参与者语言的语言特征来识别的程度,同时完成协作的不同思维任务。为此,在聊天环境中收集了78名参与者的会话对话(即,39个Dyads),同时完成三个开放式解决问题解决任务。专家评估者在流利,灵活性,阐述和原创性方面进行对话,以及三种类型的创意语言(隐喻,幽默,幽默,播放)。因子分析表明,这些分数捕获了两个主要构建体(创造力和阐述)。参与者语言的语言特征(使用自然语言处理工具捕获)占创业(R-2 = .640)和线性混合效果中的重点(R-2 = .550)分数的大量变化(LME)模型。这些结果突出了特定的语言特征,可以用于解释与创造力相关的构造的大量方差。

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