AI + 心理学

“心理学+人工智能”助力新质生产力发展

  • 傅小兰 ,
  • 纪晓恬
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  • 上海交通大学心理学院,上海 200030
傅小兰,上海交通大学心理学院首任院长、教授、博士生导师,中国科学院心理研究所原所长,中国科学院大学心理学系首任主任,国务院学位委员会学科评议组心理学组成员,《心理学报》主编,中国心理学会原理事长、原秘书长、常务理事。
*傅小兰,fuxiaolan@sjtu.edu.cn

网络出版日期: 2025-08-08

“Psychology + Artificial Intelligence” Promoting the Development of New Quality Productive Forces

  • FU Xiaolan ,
  • JI Xiaotian
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  • School of Psychology, Shanghai Jiao Tong University, Shanghai 200030, China
*FU Xiaolan, fuxiaolan@sjtu.edu.cn

Online published: 2025-08-08

摘要

“心理学+人工智能”为提升新质生产力提供了创新路径,通过优化人机协作、提升个体效能、改善组织管理、赋能群体决策以及推动技术人性化发展等方式,将显著提高生产效率、创新能力和可持续发展能力。二者融合的本质是通过技术手段实现对人性的深度理解和适配,推动生产力从机械化向人性化、智能化跃迁。在上述过程中,需坚持以人为中心的技术发展观,构建符合人类价值观的新质生产力生态系统。未来,该领域的突破不仅会体现在效率提升上,更在于能够创造更具幸福感、更能激发创造力的工作与生活模式。

本文引用格式

傅小兰 , 纪晓恬 . “心理学+人工智能”助力新质生产力发展[J]. 心理学通讯, 2025 , 8(1) : 11 -15 . DOI: 10.12100/j.issn.2096-5494.225003

Abstract

“Psychology + artificial intelligence” provides an innovative path to enhance new quality productive forces. This integration significantly improves productivity, innovation capacity, and sustainability by optimizing human-machine collaboration, enhancing individual effectiveness, improving organizational management, empowering group decision-making, and promoting the humanization of technology. The essence of the integration of “psychology + artificial intelligence” is to attain a deep understanding and adaptation of human nature through technological means, and to promote a transformation of productivity from mechanization to humanization and intelligence. In this process, it is essential to adhere to a human-centered approach to technological development and to build a new quality productive forces ecosystem aligned with human values. In the future, the breakthroughs in this field will be reflected not only in the improvement of efficiency, but also in the creation of a happier and more creative mode of work and life.

参考文献

[1] 上海交通大学. (2024). AI+HI:在世界人工智能大会上,丁奎岭校长回答了哪3个问题?2025-03-10 取自https://mp.weixin.qq.com/s/A_7uU-6sPr8G2u_qToX78w
[2] 程恩富,刘美平. (2023-11-20). 大力发展新质生产力. 北京日报, p9.
[3] Bejjanki V. R., Zhang R., Li R., Pouget A., Green C. S., Lu Z. L., ... Bavelier D. (2014). Action video game play facilitates the development of better perceptual templates.P Natl Acad Sci USA, 111(47), 16961-16966. doi: 10.1073/pnas.1417056111
[4] Cheng Y., Sanayei M., Chen X., Jia K., Li S., Fang F., ... Zhang R. (2025). A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.Nat Hum Behav, 9, 1023-1040. doi: 10.1038/s41562-025-02149-x
[5] Han S., Li X., Wei S., Zhao D., Ding J., Xu Y., ... Yuan T. (2023). Orbitofrontal cortex-hippocampus potentiation mediates relief for depression: A randomized double-blind trial and TMS-EEG study.Cell Rep Med, 4(6), 101060. doi: 10.1016/j.xcrm.2023.101060
[6] Heldmann M., Mönch E., Kesseböhmer A., Brüggemann N., Münte T. F., & Ye Z. (2023). Pramipexole modulates fronto-subthalamic pathway in sequential working memory.Neuropsychopharmacology, 48(5), 716-723. doi: 10.1038/s41386-022-01494-z
[7] Liu W., Wang C., He T., Su M., Lu Y., Zhang Y., ... Ye Z. (2021). Substantia nigra integrity correlates with sequential working memory in Parkinson's disease.J Neurosci, 41(29), 6304-6313. doi: 10.1523/JNEUROSCI.0242-21.2021
[8] Liu F., Wang P., Hu J., Shen S., Wang H., Shi C., ... Zhou A. (2025). A psychologically interpretable artificial intelligence framework for the screening of loneliness, depression, and anxiety.Appl Psychol-Hlth We, 17(1), e12639. doi: 10.1111/aphw.12639
[9] Liu F., Wang H., & Shen S. (2025). Robust dynamic facial expression recognition. IEEE Trans Biom Behav Identity Sci, (99), 1. doi: 10.1109/TBIOM.2025.3546279
[10] Liu F., Wang H., Shen S., Jia X., Hu J., Zhang J., ... Li Z. (2023). OPO-FCM: A computational affection based OCC-PAD-OCEAN federation cognitive modeling approach. IEEE T Comput Soc Sy, 10(4), 1813-1825. doi: 10.1109/TCSS.2022.3199119
[11] Liu F., Wang H., Zhang J., Fu Z., Zhou A., Qi J., ... Li Z. (2022). EvoGAN: An evolutionary computation assisted GAN.Neurocomputing, 469, 81-90. doi: 10.1016/j.neucom.2021.10.060
[12] Liu M., Zhan J., & Wang L. (2024). Specified functions of the first two fixations in face recognition: Sampling the general-to-specific facial information.iScience, 27(9), 110686. doi: 10.1016/j.isci.2024.110686
[13] Shen S., Liu F., Wang H., & Zhou A. (2025). Towards speaker-unknown emotion recognition in conversation via progressive contrastive deep supervision. IEEE T Affect Comput, (99), 1-13. doi: 10.1109/TAFFC.2025.3558222
[14] Su M., Hu K., Liu W., Wu Y., Wang T., Cao C., ... Ye Z. (2024). Theta oscillations support prefrontal-hippocampal interactions in sequential working memory.Neurosci Bull, 40(2), 147-156. doi: 10.1007/s12264-023-01134-6
[15] Su Z., Zhou X., & Wang L. (2023). Dissociated amplitude and phase effects of alpha oscillation in a nested structure of rhythm- and sequence-based temporal expectation.Cereb Cortex, 33(17), 9741-9755. doi: 10.1093/cercor/bhad240
[16] Tian W., Zhao D., Ding J., Zhan S., Zhang Y., Etkin A., ... Yuan T. (2024). An electroencephalographic signature predicts craving for methamphetamine.Cell Rep Med, 5(1), 101347. doi: 10.1016/j.xcrm.2023.101347
[17] Wang L., Baumgartner F., Kaule F. R., Hanke M., & Pollmann S. (2019). Individual face- and house-related eye movement patterns distinctively activate FFA and PPA.Nat Commun, 10(1), 5532. doi: 10.1038/s41467-019-13541-3
[18] Wang H., Li B., Wu S., Shen S., Liu F., Ding S., ... Zhou A. (2023, June). Rethinking the learning paradigm for dynamic facial expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada. doi: 10.1109/CVPR52729.2023.01722
[19] Wang Z., Meghanathan R. N., Pollmann S., & Wang L. (2024). Common structure of saccades and microsaccades in visual perception.J Vision, 24(4), 20. doi: 10.1167/jov.24.4.20
[20] Wang Z., Zhang C., Guo Q., Fan Q., & Wang L. (2024). Concurrent oculomotor hyperactivity and deficient anti-saccade performance in obsessive-compulsive disorder. J Psychiatr Res, 180, 402-410. doi: 10.1016/j.jpsychires.2024.11.013
[21] Xiao H., Lang L., Ye Z., & Wu J. (2024). Subthalamic nucleus stimulation modulates cognitive theory of mind in Parkinson's disease. Movement Disord, 39(7), 1154-1165. doi: 10.1002/mds.29830
[22] Xu Y., Yang L., You H., Zhen Z., Wang D., Wan X., ... Zhang R. (2023, August). RuleMatch: Matching Abstract Rules for Semi-supervised Learning of Human Standard Intelligence Tests. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, Macau, China. doi: 10.24963/ijcai.2023/179
[23] Yang L., You H., Zhen Z., Wang D., Wan X., Xie X., & Zhang R. (2023, July). Neural prediction errors enable abstract analogical reasoning in human standard intelligence tests. In International Conference on Machine Learning (ICML), Hawaii.
[24] Ye Z., Altena E., Nombela C., Housden C. R., Maxwell H., Rittman T., ... Rowe J. B. (2014). Selective serotonin reuptake inhibition modulates response inhibition in Parkinson's disease. Brain, 137(Pt 4), 1145-1155. doi:10.1093/brain/awu032
[25] Ye Z., Zhang G., Zhang Y., Li S., Liu N., Zhou X., ... Münte T. F. (2021). The role of the subthalamic nucleus in sequential working memory in de novo Parkinson's disease.Movement Disord, 36(1), 87-95. doi: 10.1002/mds.28344
[26] Zheng H., Zhai T., Lin X., Dong G., Yang Y., & Yuan T. (2024). The resting-state brain activity signatures for addictive disorders. Med, 5(3), 201-223.e6. doi: 10.1016/j.medj.2024.01.008
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