心理信息学：计算认知时代的研究进展&#x0002A;

童松; 陈浩; 柯罗马; 叶俊楷; 彭凯平

doi:10.16719/j.cnki.1671-6981.20250403

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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 Song^1,2, Chen Hao^3,4, 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.

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

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