2018年9月－至今 中国科学院心理研究所研究员
2010年2月－2018年8月 中国科学院心理研究所副研究员
2008年9月－2009年9月 Visiting scholar in School of Psychology, University of Sussex
2006年7月－2010年2月 中国科学院心理研究所助理研究员
内隐学习、类别学习、跨通道迁移、知觉与意识。
2025/11－今 中国家庭教育学会学习指导专业委员会，常务理事
2026/01－今 中国心理学会国际学术交流工作委员会，副主任
2021－今 心理学报编委
2025年 所讲授的认知心理学课程，荣获中国科学院大学“本科生优秀课程”
2023年 中国科学院朱李月华优秀教师奖
2019－2023年 所讲授的认知心理学课程，荣获中国科学院大学校级“研究生优秀课程”
2008年 中国科学院心理研究所所长奖学金二等奖
2005年 中国科学院研究生奖学金
2004年 中国科学院研究生奖学金
1.梁飞, 付秋芳*. (2026). 统计学习的认知神经机制. 科学通报, 71(1), 1-13.
2.Feng, T., Yao, L., Shen, X., Zhang, Y., & Fu, Q*. (2025). Form information modulates the temporal dynamics of walking direction and body orientation processing in biological motion perception. Cerebral Cortex, 35, https://doi.org/10.1093/cercor/bhaf221.
3.Yao, L., Fu, Q*., Liu, C., Wang, J., & Yi, Zhang. (2025). Distinguishing the roles of edge, color, and other surface information in basic and superordinate representation. NeuroImage 310: https://doi.org/10.1016/j.neuroimage.2025.121100.
4.Sun, Y., Yao, L., & Fu*, Q. (2024). Crossmodal correspondence mediates crossmodal transfer from visual to auditory stimuli in category learning. Journal of Intelligence 12: 80. https://doi.org/10.3390/ jintelligence12090080
5.孙莹, 孙洵伟, 王一帆, 付秋芳*. (2024). 跨通道迁移及其认知神经机制. 生物化学与生物物理进展, 51(1), 94-110.
6.Long, Z., Fu, Q., & Fu, X. (2024). How mind wandering influences motor control: The modulating role of movement difficulty. NeuroImage 294: https://doi.org/10.1016/j.neuroimage.2024.120638
7.Sun, X., & Fu, Q*. (2023). The visual advantage effect in comparing uni-modal and cross-modal probabilistic category learning. Journal of Intelligence, 11: 218. https://doi.org/10.3390/ jintelligence11120218.
8.Yao, L., Fu, Q*., & Liu, C. (2023). The roles of edge-based and surface-based information in the dynamic neural representation of objects. NeuroImage 283: https://doi.org/10.1016/j.neuroimage.2023.120425.
9.Long, Z., Fu, Q., & Fu, X. (2023). Word familiarity modulates the interference effects of mind wandering on semantic and reafferent information processing. Journal of Cognitive Neuroscience 35, 1229–1245. https://doi.org/10.1162/jocn_a_02017.
10.Sun, Y., & Fu, Q*. (2023). How do irrelevant stimuli from another modality influence responses to the targets in a same-different task. Consciousness and Cognition, 107, 103455.
11.Sun, X., Yao, L., Fu, Q*., & Fu, X. (2022). Multisensory transfer effects in implicit and explicit category learning. Psychological Research. DOI: 10.1007/s00426-022-01754-z.
12.Wu, J., & Fu*, Q. (2021). The role of working memory and visual processing in prototype category learning. Consciousness and Cognition, 94, 103176. doi: 10.1016/j.concog.2021.103176.
13.Wu, J., Li, Q., Fu*, Q., Rose, M., & Jing, L. (2021). Multisensory information facilitates the categorization of untrained stimuli. Multisensory Research, coi:10.1163/22134808-bja10061.
14.Wang, L., Feng, Y., Fu*, Q, Wang, J., Sun, X., Fu, X., Zhang, L., & Yi, Z. (2021). A Dual simple recurrent network model for chunking and abstract processes in sequence learning. Frontiers in Psychology, 12:587405. doi: 10.3389/fpsyg.2021.587405.
15.Zhou, X., Fu*, Q., & Rose, M. (2020). The role of edge-based and surface-based information in incidental category learning: Evidence from behavior and event-related potentials. Frontiers in Integrative Neuroscience, 14, 36. doi: 10.3389/fnint.2020.00036
16.Wu, J., Fu*, Q., & Rose, M. (2020). Stimulus modality influences the use of the rule-based and the similarity-based strategy in category learning. Neurobiology of Learning and Memory, 168, 1-13.
17.孙洵伟, 孙莹, 付秋芳*. (2019). 跨通道学习及其认知神经机制. 生物化学与生物物理进展, 46(6), 565-577.
18.Zhou, X., Fu*, Q., Rose, M., & Sun, Y. (2019). Which matters more in incidental category learning: Edge-based vs. surface-based features. Frontiers in Psychology, 10:183.doi: 10.3389/fpsyg.2019.00183.
19.Taesler, P., Jablonowski, J., Fu, Q., & Rose, M. (2019). Modeling implicit learning in a cross-modal audio-visual serial reaction time task. Cognitive Systems Research, 54, 154–164.
20.Fu*, Q., Sun, H., Dienes, & Z., Fu, X. (2019). Dataset of implicit sequence learning of chunking and abstract structures. Data in Brief, 22, 72–75.
21.Jablonowski, J., Taesler, P., Fu, Q., & Rose, M. (2018). Implicit acoustic sequence learning recruits the hippocampus. PLoS ONE, 13(12): e0209590. https://doi.org/10.1371/journal.pone.0209590.
22.吴洁, 付秋芳*, 周晓燕, 孙洵伟. (2018). 刺激的呈现方式对类别学习中规则和相似性知识获得的影响. 心理科学, 41(5), 1–6.
23.Fu*, Q., Sun, H., Dienes, & Z., Fu, X. (2018). Implicit sequence learning of chunking and abstract structures. Consciousness and Cognition, 62, 42–56.
24.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.
25.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.
26.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.
27.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: doi: 10.1016/j.concog.2016.06.008.
28.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.
29.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.
30.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.
31.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.
32.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
33.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.
34.Zhang, M., Fu, Q., Chen, Y. H., & Fu*, X. (2014). Emotional context influences micro-expression recognition. PLoS ONE 9(4): e95018.
35.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
36.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.
37.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, 22–34.
38.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), 1–13.
39.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.
40.Liu*, Y. J., Fu, Q., Liu, Y., & Fu, X. (2012). 2D-line-drawing-based 3D object recognition. Lecture Notes in Computer Science, 7633, 146–153.
41.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.
42.李开云, 付秋芳*, & 傅小兰. (2012). 概率类别学习的认知神经机制. 生物化学与生物物理进展, 39(11), 1037–1044.
43.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.
44.Fu*, Q., Dienes, Z., &Fu, X. (2010). Can unconscious knowledge allow control in sequence learning? Consciousness and Cognition, 19, 462-474.
45.付秋芳，傅小兰．(2010).第二任务在内隐序列学习中的作用，心理科学，19, 462-474.
46.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.
47.Liu, Y., Fu, Q., & Fu*, X. (2009). The interaction between cognition and emotion. Chinese Science Bulletin, 54, 4102-4116.
48.Fu, Q., Fu*, X., & Dienes, Z. (2008). Implicit sequence learning and conscious awareness. Consciousness and Cognition, 17, 185-202.
49.付秋芳，傅小兰．(2006).内隐序列学习与注意的关系．心理科学进展，14(6) , 817-821．
50.付秋芳，傅小兰．(2006).内隐学习中表征与意识的关系．心理科学进展，14(1), 18-22.
51.付秋芳，傅小兰．(2005).样例数量对内隐序列学习的影响．心理科学，28(4), 801-805．
52.付秋芳，刘永芳，傅小兰．(2004).知识类别和特点对内隐序列学习的影响．心理学报，36(5), 525-533．
53.付秋芳，刘永芳．(2004). 序列学习中的内隐学习效应．心理科学，27(5）, 1107-1111.
54.付秋芳，刘永芳．(2003).内隐学习潜在机制研究的某些新进展．心理科学进展，11(4), 405-410．
（论文全文下载请至http://ir.psych.ac.cn/profile/fuqf ）
1.2025年1月－2028年12月，国家自然科学基金面上项目“类别学习中跨通道迁移的认知神经机制”，负责人。
2.2025年1月－2025年12月，国家重点研发计划项目“大模型概念学习理论及关键技术研究”，心理所任务负责人。
3.2024年4月－2024年12月，教育部校外教育培训监管司课题“结合内隐与外显学习，培养中小学生科学思维能力”，负责人。
4.2021年12月－2026年11月，科技部创新2030-“脑科学与类脑研究”重大项目“意识的认知神经机制”的课题“信息整合与意识体验”，参与。
5.2020年1月－2023年12月，中德合作项目“跨模态学习的自适应、预测、交互”的子课题“内隐跨模态学习中迁移和泛化的认知神经机制”，负责人。
6.2019年12月－ 2023年12月，科技部创新2030-“新一代人工智能”重大项目“新一代认知神经网络模型”的课题“神经网络的可解释性”，负责人。
7.2016年1月－2019年12月，中德合作项目“跨模态学习的自适应、预测、交互”的子课题“跨模态预测的内隐学习的认知神经机制”，负责人。
8.2013年1月－2016年12月，国家自然科学基金面上项目“内隐类别学习的认知神经机制”，负责人。
9.2010年1月－2012年12月，国家自然科学基金青年科学基金项目“从做中学——内隐学习中无意识知识的产生及其神经机制”，负责人。
10.2007年7月－2009年7月，中国科学院心理研究所青年科学家基金“序列学习中意识和无意识知识的分离及其神经机制”，负责人。
11.2006年7月－2007年7月，中国科学院心理研究所所长基金“序列学习与意识”，负责人。
