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Qiufang Fu. Professor
  • Name: Qiufang Fu
  • Office Address: 16 Lincui Road, Chaoyang District, Beijing 100101, China 100101
  • TEL: 010-64845395
  • FAX: 010-64872070
  • E-MAIL: fuqf@psych.ac.cn

Research interests

l    Implicit learning

l    Crossmodal learning

l    Category learning

l    Unconscious knowledge

l    Subliminal perception


1. 2016.1-2019.12 "Neurocognitive mechanisms for implicit learning of crossmodal predictions" , Sub-Project of a grant funded by the National Natural Science Foundation of China and National Natural Science (NSFC 61621136008/DFG TRR-169), principle investigator.

2. 2013-2016"The cognitive and neural mechnism of implicit category learning", Fund by the National Natural Science Foundation of China (31270024), principle investigator.

3. 2010-2012 "Learning from doing---the emergence of unconscious knowledge in implicit learning and its neural correlates",  Young Scientists Fund by the National Natural Science Foundation of China (30900395), principle investigator.



9/2003 – 7/2006                Ph.D.     Cognitive Psychology, Institute of Psychology, CAS

(Advisor: Xiaolan Fu)

9/2000 – 7/2003                M. S.     Cognitive Psychology, Shandong Normal University

(Advisor: Yongfang Liu)

9/1996 – 7/2000                B. S.     School education, Qufu Normal University


9/2018 – Present                Professor, Institute of Psychology, Chinese Academy of Sciences

10/2015-10/2018                Professor, University of Chinese Academy of Sciences

2/2010 – 8/2018                 Associate Professor, Institute of Psychology, Chinese Academy of Sciences

9/2008 – 9/2009                 Visiting scholar in Schoold of Psychology, University of Sussex

7/2006 – 1/2010                 Assistant Professor, Institute of Psychology, Chinese Academy of Sciences

  1. Wu, J., Fu*, Q., Zhou, X., & Sun, X. (2018). The effect of presenting mode of different features on the acquisition of rule-based and similarity-based knowledge in category learning. Journal of Psychological Science, 41(5), 1–6. (in Chinese)
  2. Fu*, Q., Sun, H., Dienes, & Z., Fu, X. (2018). Implicit sequence learning of chunking and abstract structures. Consciousness and Cognition, 62, 4256.
  3. Zhang, M., Fu, Q., Chen, Y-H, & Fu, X. (2018). Emotional context modulates micro-expression processing as reflected in event-related potentials. PsyCh Journal, DOI: 10.1002/pchj.196.
  4. Fu*, Q., Liu, Y. J., Dienes, Z., Wu, J., Chen, W., & Fu, X. (2017). Neural Correlates of Subjective Awareness for Natural Scene Categorization of Color Photographs and Line-Drawings. Frontiers in Psychology, 8:210.doi: 10.3389/fpsyg.2017.00210.
  5. 5. Li, K., Fu*, Q., Sun, X., Zhou, X., & Fu, X. (2016). Paired-associate and feedback-based weather prediction tasks support multiple category learning systems. Frontiers in Psychology, 7:1017. doi: 10.3389/fpsyg.2016.01017.
  6. Fu*, Q., Liu, Y. J., Dienes, Z., Wu, J., Chen, W., & Fu, X. (2016). The role of edge-based and surface-based information in natural scene categorization: Evidence from behavior and event-related potentials. Consciousness and Cognition, 43, 152–166. doi: http://dx.doi.org/10.1016/j.concog.2016.06.008.
  7. Yu, M., Liu, Y. J., Wang, S. J., Fu, Q., Fu, X. (2016). A PMJ-inspired cognitive framework for natural scene categorization in line drawings. Neurocomputing, 173, 2041–2048. DOI:10.1016/j.neucom.2015.09.046.
  8. Liu, Y.,Yu, M., Fu, Q., Chen, W., Liu, Y., & Xie, L. (2016). Cognitive mechanism related to line drawings and its applications in intelligent processing of visual media: A Survey. Frontiers of Computer Science, 10(2), 216–232.
  9. Fu*, Q., Wang, J., Zhang, L., Yi, Z., & Fu*, X. (2015). Computational models of implicit sequence learning: Distinguishing abstract processes from chunking processes. In: Advances in Computational Psychophysiology (Science/AAAS, Washington, DC, 2015), p.22–24.
  10.  李婷婷付秋芳*, 傅小兰. (2015). 大学生发散性思维与创造力倾向的相关研究中华行为医学与脑科学杂志, 24(2), 166- 168.
  11. Liu*, Y. J., Yu, M., Fu, Q., Chen, W. F., Liu, Y., & Xie, L. (2014). Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey. Front. Comput. Sci. 57: 032114(15), doi: 10.1007/s11432-013-4911-9.
  12. Liu*, Y. J., Ma, C. X., Fu, Q., Fu, X., Qin, S. F., & Xie, L. (2014). A sketch-based approach for interactive organization of video clips. ACM Trans. Multimedia Comput. Commun. Appl., 11(1), Article 2, 21 pages. DOI: http://dx.doi.org/10.1145/2645643
  13. Rohrmeier, Z., Dienes, X., Guo, X., & Fu, Q. (2014). Implicit learning and recursion. In: F. Lowenthal & L. Lefebvre (Eds), Language and Recursion, Springer Verlag, pp 67-85.
  14. Zhang, M., Fu, Q., Chen, Y. H., & Fu*, X. (2014). Emotional context influences micro-expression recognition. PLoS ONE 9(4): e95018.
  15. Fu*, Q., Dienes, Z., Shang, J., & Fu, X. (2013). Who Learns More? Cultural Differences in Implicit Sequence Learning. PLoS ONE 8(8): e71625. doi:10.1371/journal.pone.0071625
  16. Shang, J., Fu*, Q., Dienes, Z., & Fu, X. (2013). Negative affects reduce performance in implicit sequence learning. PLoS ONE 8(1): e54693. doi:10.1371/journal.pone.0054693
  17. Fu*, Q., Bin, G., Dienes, Z., Fu, X, & Gao, X. (2013). Learning without consciously knowing: Evidence from event-related potentials in sequence learning. Consciousness and Cognition, 22, 2234
  18. Liu*, Y. J., Fu, Q., Liu , Y., & Fu, X. (2013). A distributed computational cognitive model for object recognition. Science in China (Series F: Information Sciences), 56(9), 113.
  19. 马翠霞, 刘永进, 付秋芳, 刘烨, 傅小兰等. (2013). 基于草图交互的视频摘要方法及认知分析. 中国科学: 信息科学, 43: 1012-1023, doi: 10.1360/112013-1.
  20. Liu*, Y. J., Fu, Q., Liu, Y., & Fu, X. (2012). 2D-line-drawing-based 3D object recognition. Lecture Notes in Computer Science, 7633, 146153.
  21. Rohrmeier*, M., Fu, Q., & Dienes, Z. (2012). Implicit learning of recursive, hierarchical grammatical structures. PLoS ONE 7(10): e45885. doi:10.1371/journal.pone.0045885.  
  22. Li, K., Fu*, Q., & Fu, X. (2012). Cognitive neural mechanism of probabilistic category learning. Progress in Biochemistry and Biophysics, 39(11), 1037–1044. (in Chinese)
  23. Fu*, Q., Dienes, Z., & Fu, X. (2010). The distinction between intuition and guessing in the SRT task generation: A reply to Norman and Price. Consciousness and Cognition, 19, 478-480.  
  24. Fu*, Q., Dienes, Z., &Fu, X. (2010). Can unconscious knowledge allow control in sequence learning? Consciousness and Cognition, 19, 462-474.   
  25.  陈文锋,禤宇明,刘烨,傅小兰,付秋芳. (2009). 创伤后应激障碍的认知功能缺陷与执行控制:5.12震后创伤恢复的认知基础心理科学进展, 17, 610-615.
  26. Liu, Y., Fu, Q., & Fu*, X. (2009). The interaction between cognition and emotion. Chinese Science Bulletin, 54, 4102-4116. 
  27. Fu, Q., Fu*, X., & Dienes, Z. (2008). Implicit sequence learning and conscious awareness. Consciousness and Cognition, 17, 185-202. 
  28. Fu*, Q., & Fu, X. (2010). The effects of secondary task on implicit sequence learning. Psychological Science, 19, 462-474.   (In Chinese)
  29. Fu, Q., & Fu*, X. (2006). Relationship between implicit sequence learning and attention. Advances in Psychological Science, 14, 817-821.  (In Chinese)
  30. Fu, Q., & Fu*, X. (2006). Relationship between representations and consciousness in implicit learning. Advances in Psychological Science, 14, 18-22. (In Chinese)
  31. Fu, Q., & Fu*, X. (2005). The effects of exemplar quantity on implicit sequence learning. Psychological Science, 28, 801-805.  (In Chinese)
  32. Fu, Q., & Liu*, Y. (2004). The effects of implicit learning on sequence learning. Psychological Science, 27, 1107-1111.  (In Chinese)
  33. Fu, Q., Liu*, Y., & Fu, X. (2004). The effects of type and feature of knowledge on implicit sequence learning. Acta Psychologica Sinica, 36, 525-533.   (In Chinese).
  34. Fu, Q., & Liu*, Y. (2003). Some new development in the research of implicit learning mechanism. Advances in Psychological Science, 11, 405-410.   (In Chinese)​​​​​​​



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