Exploring the Relationships Among Little-C, Big-C, and Divergent Thinking: A Resting-State fMRI Study
Xiaojin Liu
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorCorresponding Author
Zhenni Gao
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
Correspondence:
Zhenni Gao ([email protected])
Search for more papers by this authorXinuo Qiao
Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
Search for more papers by this authorXintong He
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorWen Liu
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorNaiyi Wang
Faculty of Education, Institute of Educational Psychology and School Counseling, Beijing Normal University, Beijing, China
Lab for Educational Neuroscience, Center for Educational Science and Technology, Faculty of Education, Beijing Normal University, Beijing, China
Search for more papers by this authorXiaojin Liu
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorCorresponding Author
Zhenni Gao
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
Correspondence:
Zhenni Gao ([email protected])
Search for more papers by this authorXinuo Qiao
Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
Search for more papers by this authorXintong He
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorWen Liu
Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
Search for more papers by this authorNaiyi Wang
Faculty of Education, Institute of Educational Psychology and School Counseling, Beijing Normal University, Beijing, China
Lab for Educational Neuroscience, Center for Educational Science and Technology, Faculty of Education, Beijing Normal University, Beijing, China
Search for more papers by this authorFunding: This work has received funding from the Scientific Research Foundation for Talented Scholars, Beijing Normal University (310432101), and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences Youth Foundation (Project No. 23YJC190009).
Xiaojin Liu and Zhenni Gao contributed equally to this work.
ABSTRACT
Previous studies tend to focus on two facets of creativity: everyday creativity (little-C) and actual creative achievement (Big-C). While little-C and Big-C both involve divergent thinking (DT), the role of DT in their relationship remains unclear. Here, we assessed the creativity scores of 64 adults, including the Creative Behavior Inventory (CBI), Creative Achievement Questionnaire (CAQ), the Abbreviated Torrance Test for Adults (ATTA), and their resting-state functional magnetic resonance imaging data. We subsequently analyzed the functional network dynamics, estimated the mediating effect of divergent thinking on the relationship between little-C and Big-C, and explored whether functional network dynamics moderate their relationship. The results showed that divergent thinking had a mediating effect on the relationship between little-C and Big-C. Dynamic neural activity in the attention and sensorimotor networks was associated with little-C, and the auditory, cognitive, and basal ganglia systems were related to Big-C. The average local efficiency of the default mode network played a moderating role in the relationship between little-C and Big-C. Our findings revealed that everyday creativity and creative achievement are interrelated, with DT playing a key role in their association.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data comes from an open dataset: https://openneuro.org/datasets/ds002330/versions/1.1.0 (OpenNeuro Dataset ds002330).
Supporting Information
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