心理信息学:计算认知时代的研究进展*

童松, 陈浩, 柯罗马, 叶俊楷, 彭凯平

心理科学 ›› 2025, Vol. 48 ›› Issue (4) : 792-803.

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心理科学 ›› 2025, Vol. 48 ›› Issue (4) : 792-803. DOI: 10.16719/j.cnki.1671-6981.20250403
计算建模与人工智能

心理信息学:计算认知时代的研究进展*

  • 童松1,2, 陈浩**3,4, 柯罗马5, 叶俊楷6, 彭凯平5
作者信息 +

Psychoinformatics: Advances and Perspectives in the Computational Cognition Era

  • Tong Song1,2, Chen Hao3,4, Ke Luoma5, Ye Junkai6, Peng Kaiping5
Author information +
文章历史 +

摘要

随着信息技术和人工智能的迅速发展,心理信息学逐渐成为心理学与信息科学之间的重要交叉领域。文章梳理了心理信息学的历史发展脉络,回顾从信息加工理论到大语言模型的关键进展,指出当代人工智能的核心特征——“计算认知”,既是识别和提取自然发生数据中认知信息的重要工具,也可作为建模和理解特定认知心理过程的研究对象。综述进一步分析了数据来源的自然化、机器学习方法的演变以及数据与理论驱动结合的趋势。在此基础上,文章探讨计算认知模型在临床、教育、文化心理学等领域的应用潜力,以揭示其对心理学研究在深度与广度上的影响。最后,结合人机协作与伦理治理,展望了心理信息学未来的发展方向,以期为该领域的理论与实践探索提供启示。

Abstract

As artificial intelligence (AI) progresses from perceptual to cognitive intelligence, psychoinformatics—an interdisciplinary field integrating psychology and information science—has entered a crucial phase of theoretical and methodological refinement. This paper reviews the historical background, theoretical foundations, methodological progress, and practical applications of psychoinformatics within the framework of computational cognition. We trace its development from early symbolic processing models to connectionist approaches and, more recently, to deep learning and large language models (LLMs), which have expanded psychological research’s scope and depth.
The paper first reviews the theoretical evolution of psychoinformatics, from Galton’s composite photography to the symbolic information processing models proposed by Simon and Newell, which conceptualized mental processes as rule-based symbolic operations. Connectionist models—particularly Rumelhart and McClelland’s parallel distributed processing, later redefined cognition as an emergent property of distributed networks, enabling more flexible modeling of psychological processes. The advent of deep learning and LLMs has shifted the field from data analysis to language-based reasoning and cognitive simulation, supporting theory-driven modeling in psychology.
The widespread use of digital technologies and the internet has enabled the collection of naturally occurring data, such as social media content and wearable device outputs, providing opportunities to study psychological phenomena in real-world contexts while raising challenges related to data quality and interpretation. Traditional machine learning models have primarily served as predictive tools to identify behavioral and cognitive patterns but often contribute little to theoretical explanation. In contrast, LLMs have shown promise in language understanding, reasoning, and generating research ideas, serving as both analytical tools and aids in theory development. Recent studies illustrate how LLMs help identify psychological concepts, suggest research directions, and illuminate cognitive processes at individual and group levels. Consequently, psychoinformatics is evolving from a purely data-driven paradigm to an integrated framework combining data and theory for explanatory and predictive psychological inquiry.
These developments signal a broader shift toward cognitive intelligence within psychoinformatics. Drawing on Newell’s time-scale framework of human action, these applications correspond to different levels of psychological functioning, from rapid interactions to long-term behavioral change. In clinical psychology, LLMs assist in the early identification of mental health risks, enable ongoing intervention through interactive systems. In educational psychology, LLM-based tutoring systems provide personalized learning, real-time motivational support, and adaptive feedback, leading to improved learning outcomes. In cross-cultural psychology, LLMs show potential in recognizing culturally specific cognitive patterns, helping researchers better understand cultural variations in thinking, emotion, and behavior, and promoting the development of more inclusive psychological theories. Finally, we outline future directions for psychoinformatics: (1) Expanding temporal and contextual models to capture both short-term psychological changes and long-term mental health patterns; (2)Enhancing human-AI collaboration in hypothesis development and theory refinement; and (3) Strengthening ethical governance by applying psychological theories and frameworks—essential for interpreting AI decisions—to guide its responsible and bounded use.
In summary, this paper suggests that psychoinformatics, guided by computational cognition, provides a useful framework for combining data-driven and theory-driven approaches. Integrating real-world data, advanced computational methods, and human-AI interaction not only increases the accuracy and practical relevance of psychological research but also opens new pathways for theoretical and applied work. Looking ahead, psychoinformatics is well-positioned to enrich the field of psychology, shaping how we understand, study, and support human action and cognition in today’s “computational cognitive” era.

关键词

心理信息学 / 计算认知 / 大语言模型 / 自然发生数据 / 人机协作

Key words

psychoinformatics / computational cognition / large language models / naturally occurring data-set / human-AI collaboration

引用本文

导出引用
童松, 陈浩, 柯罗马, 叶俊楷, 彭凯平. 心理信息学:计算认知时代的研究进展*[J]. 心理科学. 2025, 48(4): 792-803 https://doi.org/10.16719/j.cnki.1671-6981.20250403
Tong Song, Chen Hao, Ke Luoma, Ye Junkai, Peng Kaiping. Psychoinformatics: Advances and Perspectives in the Computational Cognition Era[J]. Journal of Psychological Science. 2025, 48(4): 792-803 https://doi.org/10.16719/j.cnki.1671-6981.20250403

参考文献

[1] 李宗荣. (2015). 信息心理学挑战物质心理学:理由、途径与策略. 社会科学前沿, 4(4), 283-292.
[2] 肖玲, 李宗荣. (2016). 论信息心理学的研究取向. 社会科学前沿, 5(1), 137-143.
[3] 薛婷, 陈浩, 赖凯声, 董颖红, 乐国安. (2015). 心理信息学:网络信息时代下的心理学新发展. 心理科学进展, 23(2), 325-337.
[4] Alghowinem S., Jeong S., Arias K., Picard R., Breazeal C., & Park H. W. (2021). Beyond the words: Analysis and detection of self-disclosure behavior during robot positive psychology interaction. 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). IEEE.
[5] AlHanai, T., & Ghassemi, M. (2017). Predicting latent narrative mood using audio and physiologic data. Proceedings of the AAAI Conference on Artificial Intelligence.
[6] Ali J., Shamsan M., Hezam T., & Majeed A. (2023). Impact of ChatGPT on learning motivation: Teachers and students' voices. Journal of English Studies in Arabia Felix, 2(1), 41-49.
[7] Atari M., Xue M. J., Park P. S., Blasi D., & Henrich J. (2023). Which humans? https://osf.io/5b26t
[8] Baillifard A., Gabella M., Lavenex P. B., & Martarelli C. S. (2025). Effective learning with a personal AI tutor: A case study. Education and Information Technologies, 30(1), 297-312.
[9] Bakker D., Kazantzis N., Rickwood D., & Rickard N. (2016). Mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Mental Health, 3(1), Article e7.
[10] Bakker D., Kazantzis N., Rickwood D., & Rickard N. (2018). Development and pilot evaluation of smartphone-delivered cognitive behavior therapy strategies for mood-and anxiety-related problems: MoodMission. Cognitive and Behavioral Practice, 25(4), 496-514.
[11] Bakker, D., & Rickard, N. (2019). Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission. Australian Psychologist, 54(4), 245-260.
[12] Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), Article e2218523120.
[13] Blyler, A. P., & Seligman, M. E. (2024a). AI assistance for coaches and therapists. The Journal of Positive Psychology, 19(4), 579-591.
[14] Blyler, A. P., & Seligman, M. E. (2024b). Personal narrative and stream of consciousness: An AI approach. The Journal of Positive Psychology, 19(4), 592-598.
[15] Brillouin, L. (1962). Science and information theory.Academic.
[16] Broadbent D. E.(1958). Perception and communication. Pergamon Press..
[17] Canzian, L., & Musolesi, M. (2015). Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proceedings of the 2015 ACM International joint Conference on Pervasive and Ubiquitous Computing, ACM.
[18] Carlbring P., Hadjistavropoulos H., Kleiboer A., & Andersson G. (2023). A new era in Internet interventions: The advent of Chat-GPT and AI-assisted therapist guidance. Internet Interventions, 32, Article 100621.
[19] Cichy R. M., Khosla A., Pantazis D., Torralba A., & Oliva A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific Reports, 6(1), Article 27755.
[20] Demszky D., Movshovitz-Attias D., Ko J., Cowen A., Nemade G., & Ravi S. (2020). GoEmotions: A dataset of fine-grained emotions. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
[21] Demszky D., Yang D., Yeager D. S., Bryan C. J., Clapper M., Chandhok S., Eichstaedt J. C., Hecht C., Jamieson J., & Johnson M. (2023). Using large language models in psychology. Nature Reviews Psychology, 2(11), 688-701.
[22] Du Y., Luo D., Yan R., Wang X., Liu H., Zhu H., Song Y., & Zhang J. (2024). Enhancing job recommendation through llm-based generative adversarial networks. Proceedings of the AAAI Conference on Artificial Intelligence.
[23] Duan J., Liang X., Liao J., Nakashima R., Shi H., Hu C., Kumada T., Peng K., & Tong S. (2025). "Big picture" predicts destination attractiveness: The role of physical breadth and contextual breadth. Tourism Management, 108, Article 105114.
[24] Dubey R., Hardy M. D., Griffiths T. L., & Bhui R. (2024). AI-generated visuals of car-free US cities help improve support for sustainable policies. Nature Sustainability, 7(4), 399-403.
[25] Eichstaedt J. C., Smith R. J., Merchant R. M., Ungar L. H., Crutchley P., Preoţiuc-Pietro D., Asch D. A., & Schwartz H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115(44), 11203-11208.
[26] Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19(3), 313-332.
[27] Galton, F. (1879). Composite portraits, made by combining those of many different persons into a single resultant figure. The Journal of the Anthropological Institute of Great Britain and Ireland, 8, 132-144.
[28] Goldstone, R. L., & Lupyan, G. (2016). Discovering psychological principles by mining naturally occurring data sets. Topics in Cognitive Science, 8(3), 548-568.
[29] Greer S., Ramo D., Chang Y. J., Fu M., Moskowitz J., & Haritatos J. (2019). Use of the chatbot "vivibot" to deliver positive psychology skills and promote well-being among young people after cancer treatment: Randomized controlled feasibility trial. JMIR mHealth and uHealth, 7(10), Article e15018.
[30] Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23.
[31] Grossmann I., Feinberg M., Parker D. C., Christakis N. A., Tetlock P. E., & Cunningham W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108-1109.
[32] Hill M. Q., Parde C. J., Castillo C. D., Colon Y. I., Ranjan R., Chen J. C., Blanz V., & O’Toole A. J. (2019). Deep convolutional neural networks in the face of caricature. Nature Machine Intelligence, 1(11), 522-529.
[33] Huang T. R., Cheng Y. L., & Rajaram S. (2024). Unavoidable social contagion of false memory from robots to humans. American Psychologist, 79(2), 285-298.
[34] Huang Z., Long Y., Peng K., & Tong S. (2025). An embedding-based semantic analysis approach: A preliminary study on redundancy detection in psychological concepts operationalized by scales. Journal of Intelligence, 13(1), Article 11.
[35] Jeong S., Aymerich-Franch L., Arias K., Alghowinem S., Lapedriza A., Picard R., Park H. W., & Breazeal C. (2023). Deploying a robotic positive psychology coach to improve college students' psychological well-being. User Modeling and User-Adapted Interaction, 33(2), 571-615.
[36] Ji X., Zhu T., Xue E., & Chen S. (2025). Automating social science: llms vs. human experts in variable relationship identification. https://osf.io/mkxdt_v2
[37] Jin C., Zhang S., Shu T., & Cui Z. (2023). The cultural psychology of large language models: Is ChatGPT a holistic or analytic thinker? ArXiv.
[38] Krizhevsky A., Sutskever I., & Hinton G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems.
[39] Landers R. N., Brusso R. C., Cavanaugh K. J., & Collmus A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological Methods, 21(4), 475-492.
[40] Lazer D., Pentland A., Adamic L., Aral S., Barabási A. L., Brewer D., Christakis N., Contractor N., Fowler J., & Gutmann M. (2009). Computational social science. Science, 323(5915), 721-723.
[41] Li Z., Tong S., Liu Y., Peng K., & Wang C. (2025). Potential of large language model-powered nudges for promoting daily water and energy conservation. ArXiv.
[42] Liu J. M., Li D., Cao H., Ren T., Liao Z., & Wu J. (2023). Chatcounselor: A large language models for mental health support. ArXiv.
[43] Logacheva E., Hellas A., Prather J., Sarsa S., & Leinonen J. (2024). Evaluating contextually personalized programming exercises created with generative AI. Proceedings of the 2024 ACM conference on international computing education research, ACM.
[44] Maass W., Parsons J., Purao S., Storey V. C., & Woo C. (2018). Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research. Journal of the Association for Information Systems, 19(12), 1253-1273.
[45] Markowetz A., Błaszkiewicz K., Montag C., Switala C., & Schlaepfer T. E. (2014). Psycho-informatics: Big data shaping modern psychometrics. Medical Hypotheses, 82(4), 405-411.
[46] Matz S. C., Kosinski M., Nave G., & Stillwell D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714-12719.
[47] Mehl M. R., Vazire S., Ramírez-Esparza N., Slatcher R. B., & Pennebaker J. W. (2007). Are women really more talkative than men? Science, 317(5834), 82-82.
[48] Mukherjee, A., & Chang, H. H. (2024). Heuristic reasoning in AI: Instrumental use and mimetic absorption. ArXiv.
[49] Newell, A. (1990). Unified theories of cognition. Harvard University Press..
[50] Norman, D. A., & Rumelhart, D. E. (1981). The LNR approach to human information processing. Cognition, 10(1), 235-240.
[51] Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192.
[52] O’Brien, D. T. (2024). Disentangling truth from bias in naturally occurring data. Nature Computational Science, 4(1), 5-6.
[53] Oosterhof N. N., Connolly A. C., & Haxby J. V. (2016). CoSMoMVPA: Multi-modal multivariate pattern analysis of neuroimaging data in Matlab/GNU Octave. Frontiers in Neuroinformatics, 10, Article 27.
[54] Park J. S., Popowski L., Cai C., Morris M. R., Liang P., & Bernstein M. S. (2022). Social simulacra: Creating populated prototypes for social computing systems. Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, ACM.
[55] Paxton, A., & Griffiths, T. L. (2017). Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behavior Research Methods, 49, 1630-1638.
[56] Peterson J. C., Abbott J. T., & Griffiths T. L. (2017). Adapting deep network features to capture psychological representations: An abridged report. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence.
[57] Proctor, R. W., & Xiong, A. (2020). From small-scale experiments to big data: Challenges and opportunities for experimental / psychologists. In S. E. Woo, L. Tay, & R. W. Proctor (Eds.), Big data in psychological research (pp. 35-57).APA.
[58] Rathje S., Mirea D. M., Sucholutsky I., Marjieh R., Robertson C. E., & Van Bavel, J. J. (2024). GPT is an effective tool for multilingual psychological text analysis. Proceedings of the National Academy of Sciences, 121(34), Article e2308950121.
[59] Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), Article 15.
[60] Saif N., Khan S. U., Shaheen I., ALotaibi F. A., Alnfiai M. M., & Arif M. (2024). Chat-GPT;Validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, Article 108097.
[61] Saleem F., Hamdan M., & Zalzala A. M. (2022). Towards well-being management with automated qualitative data analysis. International Joint Conference on Neural Networks.
[62] Seabrook E. M., Kern M. L., Fulcher B. D., & Rickard N. S. (2018). Predicting depression from language-based emotion dynamics: longitudinal analysis of Facebook and Twitter status updates. Journal of Medical Internet Research, 20(5), Article e168.
[63] Shah D. V., Cappella J. N., & Neuman W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6-13.
[64] Sharma A., Lin I. W., Miner A. S., Atkins D. C., & Althoff T. (2023). Human-AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1), 46-57.
[65] Simon, H. A., & Barenfeld, M. (1969). Information-processing analysis of perceptual processes in problem solving. Psychological Review, 76(5), 473-483.
[66] Simon, H. A., & Newell, A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 11-126.
[67] So J. H., Chang J., Kim E., Na J., Choi J., Sohn J. Y., Kim B. H., & Chu S. H. (2024). Aligning large language models for enhancing psychiatric interviews through symptom delineation and summarization: Pilot study. JMIR Formative Research, 8(1), Article e58418.
[68] Stojanov, A. (2023). Learning with ChatGPT 3.5 as a more knowledgeable other: An autoethnographic study. International Journal of Educational Technology in Higher Education, 20(1), Article 35.
[69] Tong, S. (2021). Informatics approaches for understanding human facial attractiveness perception and visual attention. Kyoto University.
[70] Tong S., Duan J., Liang X., Kumada T., Peng K., & Nakashima R. (2023). Inferring affective experience from the big picture metaphor: A two-dimensional visual breadth model. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
[71] Tong S., Liang X., Kumada T., & Iwaki S. (2021). Putative ratios of facial attractiveness in a deep neural network. Vision Research, 178, 86-99.
[72] Tong S., Mao K., Huang Z., Zhao Y., & Peng K. (2024). Automating psychological hypothesis generation with AI: When large language models meet causal graph. Humanities and Social Sciences Communications, 11(1), Article 896.
[73] VanRullen, R. (2017). Perception science in the age of deep neural networks. Frontiers in Psychology, 8, Article 142.
[74] Verduyn P., Lee D. S., Park J., Shablack H., Orvell A., Bayer J., Ybarra O., Jonides J., & Kross E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. Journal of Experimental Psychology: General, 144(2), 480-488.
[75] Wang A., Morgenstern J., & Dickerson J. P. (2025). Large language models that replace human participants can harmfully misportray and flatten identity groups. Nature Machine Intelligence, 7, 400-411.
[76] Webb T., Holyoak K. J., & Lu H. (2023). Emergent analogical reasoning in large language models. Nature Human Behaviour, 7(9), 1526-1541.
[77] Wei Y., Guo L., Lian C., & Chen J. (2023). ChatGPT: opportunities, risks and priorities for psychiatry. Asian Journal of Psychiatry, 90, Article 103808.
[78] Wulff, D. U., & Mata, R. (2025). Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement. Nature Human Behaviour, 9, 944-954.
[79] Yang F., Chen Z., Jiang Z., Cho E., Huang X., & Lu Y. (2023). Palr: Personalization aware llms for recommendation. ArXiv.
[80] Yarkoni, T. (2012). Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 21(6), 391-397.
[81] Youyou W., Kosinski M., & Stillwell D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4), 1036-1040.
[82] Zhao Y., Huang Z., Seligman M., & Peng K. (2024). Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots. Scientific Reports, 14(1), Article 7095.
[83] Zhou B., Lapedriza A., Khosla A., Oliva A., & Torralba A. (2017). Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1452-1464.

基金

*本研究得到南开大学文科发展基金项目(ZB22BZ0218)、中央高校基本科研业务费专项资金项目(63233147)和清华大学全球产业研究院自选课题(2021-11-09-LXHT005-01,2024-06-18-LXHT002)的资助

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