Investigating the structure of semantic networks in low and high creative persons.
Investigating the structure of semantic networks in low and high creative persons.
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DOI：
10.3389/fnhum.2014.00407
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发表时间：
2014
影响因子：
2.9
通讯作者：
Faust M
中科院分区：
文献类型：
--
作者：
Kenett YN;Anaki D;Faust M
根据梅德尼克的创造力个体差异理论，有创造力的人似乎比缺乏创造力的人拥有更丰富和更灵活的联想网络。因此，创造性个体的特征是“扁平的”(更广泛的联想)，而不是“陡峭的”(少数几个共同的联想)联想等级。为了研究这些差异，我们实现了一种新的计算方法来研究语义网络，通过分析自由联想。我们方法的核心概念是，网络中的概念通过它们的关联相关性相互关联-相似关联响应的重叠(“关联云”)。我们首先收集了一大批参与者，他们接受了几次创造力测量，并使用决策树方法将样本分为低创造力组和高创造力组。接下来，每一组都经历了自由联想生成范式，这使得我们能够构建和分析两组的语义网络。通过比较低创造能力者和高创造能力者的语义记忆网络，揭示了两者之间的差异。与高创造能力的人的语义记忆网络相比，低创造能力的人的语义记忆网络似乎更僵硬，因为它更分散，分裂成更多的子部分。我们讨论了我们的发现是如何一致的，并扩展了Mednick的理论，以及使用网络科学范式来研究高水平认知的可行性。
According to Mednick's theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations—overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's theory and the feasibility of using network science paradigms to investigate high level cognition.
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