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Dynamic Neural Network of Insight: A Functional Magnetic Resonance Imaging Study on Solving Chinese ‘Chengyu’ Riddles

Overview of attention for article published in PLOS ONE, March 2013
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Title
Dynamic Neural Network of Insight: A Functional Magnetic Resonance Imaging Study on Solving Chinese ‘Chengyu’ Riddles
Published in
PLOS ONE, March 2013
DOI 10.1371/journal.pone.0059351
Pubmed ID
Authors

Qingbai Zhao, Zhijin Zhou, Haibo Xu, Shi Chen, Fang Xu, Wenliang Fan, Lei Han

Abstract

The key components of insight include breaking mental sets and forming the novel, task-related associations. The majority of researchers have agreed that the anterior cingulate cortex may mediate processes of breaking one's mental set, while the exact neural correlates of forming novel associations are still debatable. In the present study, we used a paradigm of answer selection to explore brain activations of insight by using event-related functional magnetic resonance imaging during solving Chinese 'chengyu' (in Chinese pinyin) riddles. Based on the participant's choice, the trials were classified into the insight and non-insight conditions. Both stimulus-locked and response-locked analyses are conducted to detect the neural activity corresponding to the early and late periods of insight solution, respectively. Our data indicate that the early period of insight solution shows more activation in the middle temporal gyrus, the middle frontal gyrus and the anterior cingulate cortex. These activities might be associated to the extensive semantic processing, as well as detecting and resolving cognitive conflicts. In contrast, the late period of insight solution produced increased activities in the hippocampus and the amygdala, possibly reflecting the forming of novel association and the concomitant "Aha" feeling. Our study supports the key role of hippocampus in forming novel associations, and indicates a dynamic neural network during insight solution.

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Geographical breakdown

Country Count As %
Spain 1 2%
Colombia 1 2%
Russia 1 2%
China 1 2%
Unknown 60 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Student > Bachelor 12 19%
Researcher 6 9%
Student > Postgraduate 5 8%
Student > Master 4 6%
Other 11 17%
Unknown 13 20%
Readers by discipline Count As %
Psychology 21 33%
Arts and Humanities 6 9%
Linguistics 5 8%
Neuroscience 4 6%
Agricultural and Biological Sciences 3 5%
Other 8 13%
Unknown 17 27%