Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving
As a complex cognitive activity, knowledge transfer is mostly correlated to cognitive processes such as working memory, behavior control, and decision-making in the human brain while engineering problem-solving. It is crucial to explain how the alteration of the functional brain network occurs and h...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2021.713692/full |
| _version_ | 1818965492319125504 |
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| author | Fuhua Wang Zuhua Jiang Xinyu Li Xinyu Li Lingguo Bu Lingguo Bu Yongjun Ji |
| author_facet | Fuhua Wang Zuhua Jiang Xinyu Li Xinyu Li Lingguo Bu Lingguo Bu Yongjun Ji |
| author_sort | Fuhua Wang |
| collection | DOAJ |
| description | As a complex cognitive activity, knowledge transfer is mostly correlated to cognitive processes such as working memory, behavior control, and decision-making in the human brain while engineering problem-solving. It is crucial to explain how the alteration of the functional brain network occurs and how to express it, which causes the alteration of the cognitive structure of knowledge transfer. However, the neurophysiological mechanisms of knowledge transfer are rarely considered in existing studies. Thus, this study proposed functional connectivity (FC) to describe and evaluate the dynamic brain network of knowledge transfer while engineering problem-solving. In this study, we adopted the modified Wisconsin Card-Sorting Test (M-WCST) reported in the literature. The neural activation of the prefrontal cortex was continuously recorded for 31 participants using functional near-infrared spectroscopy (fNIRS). Concretely, we discussed the prior cognitive level, knowledge transfer distance, and transfer performance impacting the wavelet amplitude and wavelet phase coherence. The paired t-test results showed that the prior cognitive level and transfer distance significantly impact FC. The Pearson correlation coefficient showed that both wavelet amplitude and phase coherence are significantly correlated to the cognitive function of the prefrontal cortex. Therefore, brain FC is an available method to evaluate cognitive structure alteration in knowledge transfer. We also discussed why the dorsolateral prefrontal cortex (DLPFC) and occipital face area (OFA) distinguish themselves from the other brain areas in the M-WCST experiment. As an exploratory study in NeuroManagement, these findings may provide neurophysiological evidence about the functional brain network of knowledge transfer while engineering problem-solving. |
| first_indexed | 2024-12-20T13:17:52Z |
| format | Article |
| id | doaj.art-63e47bd1fbb0480aa1f65309214fb9dc |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-20T13:17:52Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-63e47bd1fbb0480aa1f65309214fb9dc2022-12-21T19:39:29ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612021-10-011510.3389/fnhum.2021.713692713692Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-SolvingFuhua Wang0Zuhua Jiang1Xinyu Li2Xinyu Li3Lingguo Bu4Lingguo Bu5Yongjun Ji6Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai, ChinaSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, SingaporeJoint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, ChinaSchool of Software, Shandong University, Jinan, ChinaDepartment of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, ChinaAs a complex cognitive activity, knowledge transfer is mostly correlated to cognitive processes such as working memory, behavior control, and decision-making in the human brain while engineering problem-solving. It is crucial to explain how the alteration of the functional brain network occurs and how to express it, which causes the alteration of the cognitive structure of knowledge transfer. However, the neurophysiological mechanisms of knowledge transfer are rarely considered in existing studies. Thus, this study proposed functional connectivity (FC) to describe and evaluate the dynamic brain network of knowledge transfer while engineering problem-solving. In this study, we adopted the modified Wisconsin Card-Sorting Test (M-WCST) reported in the literature. The neural activation of the prefrontal cortex was continuously recorded for 31 participants using functional near-infrared spectroscopy (fNIRS). Concretely, we discussed the prior cognitive level, knowledge transfer distance, and transfer performance impacting the wavelet amplitude and wavelet phase coherence. The paired t-test results showed that the prior cognitive level and transfer distance significantly impact FC. The Pearson correlation coefficient showed that both wavelet amplitude and phase coherence are significantly correlated to the cognitive function of the prefrontal cortex. Therefore, brain FC is an available method to evaluate cognitive structure alteration in knowledge transfer. We also discussed why the dorsolateral prefrontal cortex (DLPFC) and occipital face area (OFA) distinguish themselves from the other brain areas in the M-WCST experiment. As an exploratory study in NeuroManagement, these findings may provide neurophysiological evidence about the functional brain network of knowledge transfer while engineering problem-solving.https://www.frontiersin.org/articles/10.3389/fnhum.2021.713692/fullknowledge transferfunctional connectivitybrain networkwavelet phase coherencefunctional near-infrared spectroscopycognitive structure |
| spellingShingle | Fuhua Wang Zuhua Jiang Xinyu Li Xinyu Li Lingguo Bu Lingguo Bu Yongjun Ji Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving Frontiers in Human Neuroscience knowledge transfer functional connectivity brain network wavelet phase coherence functional near-infrared spectroscopy cognitive structure |
| title | Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving |
| title_full | Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving |
| title_fullStr | Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving |
| title_full_unstemmed | Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving |
| title_short | Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving |
| title_sort | functional brain network analysis of knowledge transfer while engineering problem solving |
| topic | knowledge transfer functional connectivity brain network wavelet phase coherence functional near-infrared spectroscopy cognitive structure |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2021.713692/full |
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