Dialogue between mind and algorithm: Deep symbiosis of psychology and artificial intelligence

Authors

• Xiaolan FU, School of Psychology, Shanghai Jiao Tong University, Shanghai 200030, ChinaFollow
• Zheng YAN, Department of Educational and Counseling Psychology, University at Albany, State University of New York, Albany 12222, USA

Keywords

psychology, artificial intelligence, mutual empowerment, deep symbiosis, human-AI interaction

Abstract

As artificial intelligence (AI) evolves from a supportive tool into a collaborative partner, the convergence of psychology and AI is gradually shifting from one-way application toward deep symbiosis. This study discusses the mutual empowerment resulting from their interaction, as well as the challenges they face and potential pathways to breakthroughs. On the one hand, psychology empowers AI by enhancing its human-like intelligence and social adaptability through cognitive modeling and ethical constraints; on the other hand, AI empowers psychology by leveraging multimodal data and algorithmic models to revolutionize psychological assessment and intervention methods. This deep symbiosis requires a clear-eyed acknowledgment of tensions, a prudent delineation of boundaries, and a candid recognition of risks. Future research should focus on cognitive complementarity, emotional resonance, and ethical alignment, enabling AI to serve as a mirror for humans to deepen their self-awareness, and to build an innovative ecosystem of resonance between humans and intelligent agents, achieving a higher level of synergy between technology and the humanities amidst their differences.

First page

1040

Last Page

1054

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

References

1. Licklider J C R. Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, 1960, HFE-1(1): 4-11.

2. 林崇德. 人与人工智能和谐共生——读《创造力与人工智能》. 光明日报, 2025-07-10(11). Lin C D. Humans and artificial intelligence: A harmonious coexistence—Reflections on Creativity and Artificial Intelligence. Guangming Daily, 2025-07-10(11). (in Chinese)

3. Broadbent D E. Perception and Communication. London: Pergamon Press, 1958.

4. Neisser U. Cognitive Psychology. New York: Appleton-Century-Crofts, 1967.

5. Newell A, Simon H A. Human Problem Solving. Upper Saddle River: Prentice Hall, Inc, 1972.

6. Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 1962, 160(1): 106-154.

7. Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 1980, 36(4): 193-202.

8. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks// Advances in Neural Information Processing Systems 25 (NIPS 2012). Red Hook: Curran Associates, Inc, 2012: 1097-1105.

9. Hebb D O. The Organization of Behavior: A Neuropsychological Theory. New York: John and Sons, 1949.

10. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958, 65(6): 386-408.

11. Rumelhart D E, McClelland J L, PDP Research Groupthe. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations. Cambridge: MIT Press, 1986.

12. Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998.

13. Sutton R S, Barto A G. Reinforcement Learning: An Introduction (2nd ed.). Cambridge: MIT Press, 2018.

14. Thorndike E L. Animal Intelligence: An Experimental Study of the Associative Processes in Animals. New York: Columbia University Press, 1898.

15. Thorndike E L. Animal Intelligence: Experimental Studies. New York: The Macmillan Company, 1911.

16. Skinner B F. The Behavior of Organisms: An Experimental Analysis. New York: Appleton-Century-Crofts, 1938.

17. Skinner B F. Science and Human Behavior. New York: Macmillan, 1953.

18. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. (2014-09-01)[2026-04-02]. https://arxiv.org/abs/1409.0473..

19. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// Guyon I, Luxburg U V, Bengio S, et al. Advances in Neural Information Processing Systems 30. Red Hook: Curran Associates, Inc, 2017: 5998-6008.

20. Weizenbaum J. ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 1966, 9(1): 36-45.

21. Quintana L R, Crowell C R, Pryor J B, et al. Human-computer interaction: A preliminary social psychological analysis. Behavior Research Methods & Instrumentation, 1982, 14(2): 210-220.

22. Turner P. A Psychology of User Experience: Involvement, Affect and Aesthetics. Cham: Springer International Publishing, 2017.

23. 罗莉娟, 王康, 胡金淼, 等. 当人工智能面对人类情感：服务机器人情感表达对用户体验的影响机制. 心理科学进展, 2025, 33(6): 1006-1026. Luo L J, Wang K, Hu J M, et al. When artificial intelligence faces human emotions: The impact mechanism of emotion expression in AI-empowered service robots on user experience. Advances in Psychological Science, 2025, 33(6): 1006-1026. (in Chinese)

24. 解煜彬, 周荣刚. 新型人机关系下的人机双向信任. 心理科学进展, 2025, 33(6): 916-932. Xie Y B, Zhou R G. The bidirectional trust in the context of new human-machine relationships. Advances in Psychological Science, 2025, 33(6): 916-932. (in Chinese)

25. Chen X J, Zhang J Y, Zhao Y X, et al. PATE model: A 30-year review and analysis of gestural interaction research. Human Factors, 2026, 68(1): 42-77.

26. Kosinski M. Evaluating large language models in theory of mind tasks. PNAS, 2024, 121(45): e2405460121.

27. Strachan J W A, Albergo D, Borghini G, et al. Testing theory of mind in large language models and humans. Nature Human Behaviour, 2024, 8(7): 1285-1295.

28. 杜传晨, 郑远霞, 郭倩倩, 等. 大语言模型的人工心理理论：证据、界定与挑战. 心理科学进展, 2025, 33(12): 2027-2042. Du C C, Zheng Y X, Guo Q Q, et al. Artificial theory of mind in large language models: Evidence, conceptualization, and challenges. Advances in Psychological Science, 2025, 33(12): 2027-2042. (in Chinese)

29. Wu Y H, Guo W T, Liu Z R, et al. How large language models encode theory-of-mind: A study on sparse parameter patterns. npj Artificial Intelligence, 2025, 1: 20.

30. Wickramasinghe D, Ambegoda T. Commonsense-driven symbolic ReAct-NLI prompting (CSR-NLI) for causal analysis of mental health issues in workspace communication; advancements in the CAMS dataset// Proceedings of Applied Data Science & Artificial Intelligence Symposium 2025. Moratuwa: University of Moratuwa, 2025: 43.

31. Ke L M, Tong S, Cheng P, et al. Exploring the frontiers of LLMs in psychological applications: A comprehensive review. Artificial Intelligence Review, 2025, 58(10): 305.

32. El Hmimdi A E, Kapoula Z. Distinguishing dyslexia, attention deficit, and learning disorders: Insights from AI and eye movements. Bioengineering, 2025, 12(7): 737.

33. Nie J P, Shao H V, Fan Y A, et al. LLM-based conversational AI therapist for daily functioning screening and psychotherapeutic intervention via everyday smart devices. ACM Transactions on Computing for Healthcare, 2025: 3712299.

34. 黄峰, 丁慧敏, 李思嘉, 等. 基于大语言模型的自助式AI心理咨询系统构建及其效果评估. 心理学报, 2025, 57(11): 2022-2042. Huang F, Ding H M, Li S J, et al. Self-help AI psychological counseling system based on large language models and its effectiveness evaluation. Acta Psychologica Sinica, 2025, 57(11): 2022-2042. (in Chinese)

35. Shah A, Hooshyar D, Tomberg V. From wrists and pockets to interventions: Leveraging digital biomarkers to infer psychosocial factors—A systematic review. ACM Transactions on Computing for Healthcare, 2026, 7(2): 32.

36. Strachan J W A, Albergo D, Borghini G, et al. Testing theory of mind in large language models and humans. Nature Human Behaviour, 2024, 8(7): 1285-1295.

37. Cui Z Y, Li N, Zhou H K. A large-scale replication of scenario-based experiments in psychology and management using large language models. Nature Computational Science, 2025, 5(8): 627-634.

38. Polachek S W, Romano K, Tonguc O. Homo-silicus: Not (yet) a good imitator of homo sapiens or homo economicus. Journal of the Economic Science Association, 2025, 11(2): 261-269.

39. Hintze A, Adami C. Promoting cooperation in the public goods game using artificial intelligent agents. npj Complexity, 2026, 3: 3.

40. Qian C, Tsai V, Behr M, et al. Deliberate Lab: A platform for real-time human-AI social experiments. (2025-10-14)[2026-04-02]. https://arxiv.org/abs/2510.13011.

41. Ulakci E, Göbel J P, Huff M. Towards a psychology of machines: Large language models predict human memory. PeerJ Computer Science, 2025, 11: e3324.

42. Lin Z C. Large language models as psychological simulators: A methodological guide. Advances in Methods and Practices in Psychological Science, 2026, 9: 25152459251410153.

43. Golden A, Aboujaoude E. Describing the framework for AI tool assessment in mental health and applying it to a generative AI obsessive-compulsive disorder platform: Tutorial. JMIR Formative Research, 2024, 8: e62963.

44. Stade E C, Eichstaedt J C, Kim J P, et al. Readiness evaluation for artificial intelligence-mental health deployment and implementation (READI): A review and proposed framework. Technology, Mind, and Behavior, 2025, 6(2): 111-122.

45. Piaget J. Genetic Epistemology. New York: Columbia University Press, 1970.

46. Fitts P M. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 1954, 47(6): 381-391.

47. 李萌. 当前人工智能发展和治理中几个热点问题的认识. 中国科学院院刊, 2026, 41(3): 435-441. Li M. Understanding key issues in current artificial intelligence development and governance. Bulletin of Chinese Academy of Sciences, 2026, 41(3): 435-441. (in Chinese)

48. Newell A, Simon H. The logic theory machine—A complex information processing system. IEEE Transactions on Information Theory, 1956, 2(3): 61-79.

49. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436-444.

50. Hassabis D, Kumaran D, Summerfield C, et al. Neuroscience-inspired artificial intelligence. Neuron, 2017, 95(2): 245-258.

51. Frank M R, Sun L J, Cebrian M, et al. Small cities face greater impact from automation. Journal of the Royal Society Interface, 2018, 15: 20170946.

52. Frank M R, Autor D, Bessen J E, et al. Toward understanding the impact of artificial intelligence on labor. PNAS, 2019, 116(14): 6531-6539.

53. Longoni C, Bonezzi A, Morewedge C K. Resistance to medical artificial intelligence. Journal of Consumer Research, 2019, 46(4): 629-650.

54. Rahwan I, Cebrian M, Obradovich N, et al. Machine behaviour. Nature, 2019, 568: 477-486.

55. De Freitas J, Agarwal S, Schmitt B, et al. Psychological factors underlying attitudes toward AI tools. Nature Human Behaviour, 2023, 7(11): 1845-1854.

56. Demszky D, Yang D, Yeager D S, et al. Using large language models in psychology. Nature Reviews Psychology, 2023, 2(11): 688-701.

57. Binz M, Akata E, Bethge M, et al. A foundation model to predict and capture human cognition. Nature, 2025, 644: 1002-1009.

58. Cheng M, Lee C, Khadpe P, et al. Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 2026, 391: eaec8352.

59. Frank M C, Goodman N D. Cognitive modeling using artificial intelligence. Annual Review of Psychology, 2026, 77: 543-566.

60. 彭玉佳, 方方. 人工智能大模型发展趋势. 光明日报, 2025-10-21(11). Peng Y J, Fang F. Trends in the development of large AI models. Guangming Daily, 2025-10-21(11). (in Chinese)

Recommended Citation

FU, Xiaolan and YAN, Zheng (2026) "Dialogue between mind and algorithm: Deep symbiosis of psychology and artificial intelligence," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 41 : Iss. 5 , Article 19.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20260402003
Available at: https://bulletinofcas.researchcommons.org/journal/vol41/iss5/19