生成式AI使用模式对创造力的影响：基于静息态功能连接的证据
The Impact of Generative AI Usage Patterns on Creativity: Evidence from Resting-State Functional Connectivity
DOI: 10.12677/ap.2026.165239, PDF,    科研立项经费支持
作者: 程麟哲：天津师范大学心理学部，天津；吴 瑕*：天津师范大学心理学部，天津；教育部人文社会科学重点研究基地天津师范大学心理与行为研究院，天津；学生心理发展与学习天津市高校社会科学实验室，天津
关键词: 生成式人工智能；AI使用；创造力；好奇性；Generative Artificial Intelligence； AI Usage； Creativity； Curiosity
摘要: 目的：探究生成式AI使用模式(对AI局限性的认知)与个体创造力的关系及其神经机制。方法：招募129名大学生，采用问卷评估AI使用模式与创造力好奇性，并采集静息态功能磁共振数据，计算局部一致性(ReHo)指标。通过全脑相关与中介分析检验神经活动的中介作用。结果：AI使用模式与创造力好奇性显著正相关(r = 0.26)，与右侧海马体ReHo显著正相关(r = 0.18)。右侧海马体ReHo在二者间起显著部分中介作用，间接效应值为0.06。结论：批判型AI使用模式通过增强右侧海马体的自发神经活动，提升个体的好奇性，进而影响个体创造力，揭示了AI使用影响创造力的认知神经路径。
Abstract: Objective: To investigate the relationship between generative AI usage patterns (awareness of AI limitations) and individual creativity, as well as its underlying neural mechanisms. Methods: A total of 129 university students were recruited. Questionnaires were used to assess AI usage patterns and creative curiosity, and resting-state functional magnetic resonance imaging data were collected to calculate the regional homogeneity (ReHo) index. Whole-brain correlation and mediation analyses were employed to examine the mediating role of neural activity. Results: AI usage patterns showed a significant positive correlation with creative curiosity (r = 0.26) and a significant positive correlation with ReHo in the right hippocampus (r = 0.18). ReHo in the right hippocampus played a significant partial mediating role between AI usage patterns and creative curiosity, with an indirect effect value of 0.06. Conclusion: Critical AI usage patterns enhance individual curiosity and subsequently influence individual creativity by increasing spontaneous neural activity in the right hippocampus, revealing a cognitive neural pathway through which AI usage affects creativity.
文章引用：程麟哲, 吴瑕 (2026). 生成式AI使用模式对创造力的影响：基于静息态功能连接的证据. 心理学进展, 16(5), 73-82. https://doi.org/10.12677/ap.2026.165239

参考文献

[1] 胡钦太, 梁心贤, 刘颜帆, 王姝莉(2025). 生成式人工智能如何影响学生发展——基于31项实验与准实验研究的元分析. 现代远程教育研究, 37(2), 83-91.
[2] 梁宇畅, 何刚, 金孟子(2024). 使用生成式人工智能对员工创造力评价的影响. 外国经济与管理, 46(10), 71-88+104.
[3] 刘晓陵, 刘路, 邱燕霞, 金瑜, 周隽(2016). 威廉斯创造力测验的信效度检验. 基础教育, 13(3), 51-58.
[4] 周浩, 龙立荣(2004). 共同方法偏差的统计检验与控制方法. 心理科学进展, 12(6), 942-950.
[5] Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative Cognition and Brain Network Dynamics. Trends in Cognitive Sciences, 20, 87-95.[CrossRef] [PubMed]
[6] Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q. et al. (2018). Robust Prediction of Individual Creative Ability from Brain Functional Connectivity. Proceedings of the National Academy of Sciences, 115, 1087-1092.[CrossRef] [PubMed]
[7] Becker, M., Sommer, T., & Cabeza, R. (2025). Insight Predicts Subsequent Memory via Cortical Representational Change and Hippocampal Activity. Nature Communications, 16, Article No. 4341.[CrossRef] [PubMed]
[8] Bervar, M., Bertoncel, T., & Pejić Bach, M. (2026). Generative Artificial Intelligence and the Creative Industries: A Bibliometric Review and Research Agenda. Systems, 14, Article 138.[CrossRef] 
[9] Chan, C. K. Y., & Zhou, W. (2023). An Expectancy Value Theory (EVT) Based Instrument for Measuring Student Perceptions of Generative Ai. Smart Learning Environments, 10, Article No. 64.[CrossRef] 
[10] Ejaz, A., Farhan, M., Ernesto, F., & Longa, A. (2025). AI and Cognitive Load: How Reliance on AI Tools (ChatGPT, etc.) Affects Critical Thinking. Research Journal of Psychology, 3, 1-10.
[11] Evans, N. S., & Jirout, J. J. (2022). Investigating the Relation between Curiosity and Creativity. Journal of Creativity, 33, Article 100038.[CrossRef] 
[12] Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y. et al. (2025). Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance. British Journal of Educational Technology, 56, 489-530.[CrossRef] 
[13] Gilbert, S. J. (2024). Cognitive Offloading Is Value-Based Decision Making: Modelling Cognitive Effort and the Expected Value of Memory. Cognition, 247, Article 105783.[CrossRef] [PubMed]
[14] Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of Curiosity Modulate Hippocampus-Dependent Learning via the Dopaminergic Circuit. Neuron, 84, 486-496.[CrossRef] [PubMed]
[15] Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How Does Generative Artificial Intelligence Impact Student Creativity? Journal of Creativity, 34, Article 100072.[CrossRef] 
[16] Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007). Patients with Hippocampal Amnesia Cannot Imagine New Experiences. Proceedings of the National Academy of Sciences, 104, 1726-1731.[CrossRef] [PubMed]
[17] Hayes, A. F., & Scharkow, M. (2013). The Relative Trustworthiness of Inferential Tests of the Indirect Effect in Statistical Mediation Analysis: Does Method Really Matter? Psychological Science, 24, 1918-1927.[CrossRef] [PubMed]
[18] Jarrahi, M. H., Lutz, C., & Newlands, G. (2022). Artificial Intelligence, Human Intelligence and Hybrid Intelligence Based on Mutual Augmentation. Big Data & Society, 9, 1-6.[CrossRef] 
[19] Kumaran, D., & Maguire, E. A. (2007). Match-Mismatch Processes Underlie Human Hippocampal Responses to Associative Novelty. The Journal of Neuroscience, 27, 8517-8524.[CrossRef] [PubMed]
[20] Lee, J. D., & See, K. A. (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46, 50-80.[CrossRef] 
[21] Meliss, S. (2022). Remember the Magic? How Curiosity Elicitation and the Availability of Extrinsic Incentives Shape Memory Formation and Its Neural Mechanisms during Encoding and Early Consolidation. PhD Thesis, University of Reading.
[22] Microsoft (2026). AI Diffusion Report 2025 for Distribution: Microsoft AI Diffusion Report.
https://www.microsoft.com/en-us/research/wp-content/uploads/2025/10/Microsoft-AI-Diffusion-Report.pdf
[23] Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24, 167-202.[CrossRef] [PubMed]
[24] Murty, V. P., Ballard, I. C., Macduffie, K. E., Krebs, R. M., & Adcock, R. A. (2013). Hippocampal Networks Habituate as Novelty Accumulates. Learning & Memory, 20, 229-235.[CrossRef] [PubMed]
[25] Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56, 81-103.[CrossRef] 
[26] Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology, 63, 539-569.[CrossRef] [PubMed]
[27] Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20, 676-688.[CrossRef] [PubMed]
[28] Scoville, W. B., & Milner, B. (1957). Loss of Recent Memory after Bilateral Hippocampal Lesions. Journal of Neurology, Neurosurgery & Psychiatry, 20, 11-21.[CrossRef] [PubMed]
[29] Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2025). Generative Artificial Intelligence: A Systematic Review and Applications. Multimedia Tools and Applications, 84, 23661-23700.[CrossRef] 
[30] Shen, M. W. (2022). Trust in AI: Interpretability is Not Necessary or Sufficient, While Black-Box Interaction Is Necessary and Sufficient.[CrossRef] 
[31] Small, G. W., Lee, J., Kaufman, A., Jalil, J., Siddarth, P., Gaddipati, H. et al. (2020). Brain Health Consequences of Digital Technology Use. Dialogues in Clinical Neuroscience, 22, 179-187.[CrossRef] [PubMed]
[32] Sternberg, R. J., & Lubart, T. I. (1996). Investing in Creativity. American Psychologist, 51, 677-688.[CrossRef] 
[33] Strange, B. A., & Dolan, R. J. (2006). Anterior Medial Temporal Lobe in Human Cognition: Memory for Fear and the Unexpected. Cognitive Neuropsychiatry, 11, 198-218.[CrossRef] [PubMed]
[34] Sun, S., Li, Z. A., Foo, M., Zhou, J., & Lu, J. G. (2025). How and for Whom Using Generative AI Affects Creativity: A Field Experiment. Journal of Applied Psychology, 110, 1561-1573.[CrossRef] [PubMed]
[35] Torrance, E. P. (1972). Predictive Validity of the Torrance Tests of Creative Thinking. The Journal of Creative Behavior, 6, 236-262.[CrossRef] 
[36] Williams, F. E. (1980). Creativity Assessment Packet. DOK Publishers.
[37] Yan, C. G., & Zang, Y. F. (2010). DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI. Frontiers in System Neuroscience, 4, Article 13.
[38] Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional Homogeneity Approach to fMRI Data Analysis. NeuroImage, 22, 394-400.[CrossRef] [PubMed]