Yang Wenjing
Biographic Data
| ID | 5378605 |
|---|---|
| NAME | Yang Wenjing |
| GIVEN NAMES | Yang |
| FAMILY NAME | Wenjing |
| SIGNATURE | WENJING Y |
| AFFILIATIONS | Southwest University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Resting-state functional connectome predicts individual differences in depression during Covid-19 pandemic
Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The result…
The effect of prototype difficulty and semantic similarity on the prototype activation
摘要: 采用现实生活中的科学发明事例, 通过两个研究探讨了问题先导下的原型启发促发顿悟的机制。实验1采用简单原型材料, 利用“先问题”范式探讨了问题先导下的原型启发促发顿悟的关键认知过程, 结果发现问题激活率可以解释问题解决正确率89.3%的变异。实验2采用3种不同难度的原型材料, 用“先问题”范式和被试自我报告问题和原型中关键词的方式探讨问题自动激活的机制。结果发现原型和问题关键词的提取对问题激活率有显著影响, 而原型和问题关键词之间的语义相似性与问题激活率显著相关。研究表明, 问题激活是现实生活中广泛存在的问题先导下的原型启发促发顿悟的关键认知过程。原型的特征性功能和问题的需求性功能之间的语义相似性是问题自动激活的机制
No prominent works on this page.
The effect of prototype difficulty and semantic similarity on the prototype activation
摘要: 采用现实生活中的科学发明事例, 通过两个研究探讨了问题先导下的原型启发促发顿悟的机制。实验1采用简单原型材料, 利用“先问题”范式探讨了问题先导下的原型启发促发顿悟的关键认知过程, 结果发现问题激活率可以解释问题解决正确率89.3%的变异。实验2采用3种不同难度的原型材料, 用“先问题”范式和被试自我报告问题和原型中关键词的方式探讨问题自动激活的机制。结果发现原型和问题关键词的提取对问题激活率有显著影响, 而原型和问题关键词之间的语义相似性与问题激活率显著相关。研究表明, 问题激活是现实生活中广泛存在的问题先导下的原型启发促发顿悟的关键认知过程。原型的特征性功能和问题的需求性功能之间的语义相似性是问题自动激活的机制
Resting-state functional connectome predicts individual differences in depression during Covid-19 pandemic
Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The result…
Artificial Intelligence (1 works) · Biology (1 works) · Clinical Psychology (1 works) · Cognition (1 works) · Computer Science (1 works) · Connectome (1 works) · Coronavirus disease 2019 (COVID-19 (1 works) · Data mining (1 works) · Depression (economics (1 works) · Disease (1 works)