Psychoinformatics: Advances and Perspectives in the Computational Cognition Era

Tong Song; Chen Hao; Ke Luoma; Ye Junkai; Peng Kaiping

doi:10.16719/j.cnki.1671-6981.20250403

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Journal of Psychological Science ›› 2025, Vol. 48 ›› Issue (4) : 792-803. DOI: 10.16719/j.cnki.1671-6981.20250403

Computational modeling and artificial intelligence

Psychoinformatics: Advances and Perspectives in the Computational Cognition Era

Tong Song^1,2, Chen Hao^3,4, Ke Luoma⁵, Ye Junkai⁶, Peng Kaiping⁵

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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.

Key words

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

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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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