Editorial: Innovations in Clinical Psychological Science in the Era of Complexity

Abstract

In 2000, the theoretical physicist Stephen Hawking stated, “I think the next [21st] century will be the century of complexity” (Chiu, 2000, p. 29A). His prediction appears accurate. Over the course of this century, the world has faced numerous complex and uncertain challenges at a global scale, including extreme climate change, species extinction, the spread of fake news, and the COVID-19 pandemic. Modern science has become increasingly interdisciplinary and is leveraging advanced technologies to address these crises and to better understand the complex systems underlying them. The most striking example is the development of network science (Barabási, 2016), which provides visualizations of diverse complex systems explored across various academic disciplines (e.g., climatology, bioecology, socio-informatics, and infectious disease epidemiology) and seeks to explain how extreme phenomena arise from these systems. Also noteworthy is the growing application of artificial intelligence (AI)-related technologies. As exemplified by the announcement that the Nobel Prizes in Physics and Chemistry in 2024 were awarded to pioneers in AI research (Royal Swedish Academy of Sciences, 2024a, 2024b), AI-related technologies are now being extensively utilized to identify predictable patterns in complex phenomena that often escape human awareness.

Waves of complexity are also emerging in the field of clinical psychology. As Jennifer Tackett, editor of Clinical Psychological Science, noted, clinical psychology is increasingly striving for integration with various subfields, both within and beyond psychology, to foster innovation (Association for Psychological Science [APS], 2020). In this century, we have observed the growing application of complex systems or network approaches to investigate psychopathology (for reviews, see Borsboom, Deserno, et al., 2021; Robinaugh et al., 2020) and the increasing use of machine learning algorithms to improve the prediction of clinical outcomes (Dwyer et al., 2018; Hilbert et al., 2020). The use of neuroscientific measures has gained popularity in clinical psychology (Hajcak et al., 2017), while smartphones and other digital devices have expanded the designs of mental health research, including studies employing ecological momentary assessment methods (Fried et al., 2022; Larson & Csikszentmihalyi, 1983). To borrow the words of Jennifer Tackett (APS, 2020), clinical psychology is striving to establish itself as a "hub of the hub" science referred to as clinical psychological science. Psychology, as a whole, has established itself as a hub science referred to as psychological science, characterized by the growing use of multidisciplinary methodologies. Influential clinical psychology researchers are now applying these methodologies to investigate mental disorders and other complex phenomena related to mental health.

To advance the movement toward interdisciplinary clinical psychological science, we launched this special issue and accepted six papers that explore innovations in various areas. The review paper by Kashihara et al. (2025) discusses the clinical application of the psychological network approach (Borsboom, Deserno, et al., 2021; Robinaugh et al., 2020), which potentially facilitates personalized treatments of mental disorders, and a five-step model is provided to bridge the gap between the academic field and clinical settings. From their broad perspective, which includes the use of narrative network models as a stepping-stone for clinicians and the collaborative development of clinical guidelines, readers will recognize that collaboration among clinical scientists, clinicians, and clients is fundamental to the successful clinical application of the network approach. The next paper by Omizu and Kunisato (2025) also highlights the potential of the psychological network approach. They incorporated treatment component nodes into the formal network model of depression developed by Cramer et al. (2016) and conducted mathematical simulations to identify treatment strategies effective for deactivating depressive symptoms. The results demonstrated that strategies aimed at intervening in multiple symptoms with low centrality, as well as selective interventions targeting a single symptom with high centrality, were effective in reducing overall symptomatology. These findings from their simulations contribute to advancing the discussion on how to develop effective treatment strategies for depression from the network perspective.

The next two papers emphasize advanced measurement technologies. Iwayama et al. (2025) focus on the clinical utility of near-infrared spectroscopy (NIRS), a noninvasive neuroimaging technique with ecological validity in certain contexts (Irani et al., 2007; Strangman et al., 2002). They conducted a scoping literature review to identify previous studies that used NIRS to examine interactive dynamics in psychotherapies. Their review of the seven identified papers provides readers with an understanding of the current state of this research area and highlights the need for future studies to use NIRS to capture changes in brain function induced by micro-level events during psychotherapy. Uchida and Kurosawa (2025), in contrast, focus on the use of wearable trackers to monitor sleep patterns. They recruited 50 university students and used questionnaires and wearable trackers to measure their subjective and objective sleep patterns. Their descriptive analyses highlighted differences between subjective and objective sleep patterns in predicting quality of life and other mental health outcomes. These findings align with previous studies that reported similar predictive differences (Regestein et al., 2004; Thorburn-Winsor et al., 2022) and emphasize the importance of using wearable trackers alongside questionnaires to capture different aspects of sleep patterns.

The remaining two papers in this issue pursued ambitious goals. Takeshige et al. (2025) aimed to detect depression through mobile sensing and conducted an initial exploratory study using machine learning algorithms. Although their predictive model demonstrated lower diagnostic performance compared to previous laboratory studies (Richter et al., 2020, 2021), their work provides valuable inspiration for clinical scientists to leverage mobile-based cognitive function tasks and machine learning algorithms for the early detection of psychopathology. Ono et al. (2025), in contrast, explored the use of search-based advertisements for suicide prevention. In their pilot study, advertisements were displayed to Internet users who searched for terms related to several predefined mental health issues (e.g., depression, domestic violence, and addiction), and the click rates for links directing users to websites with supporting information were recorded. Although the suicide-prevention effects of the advertisements remain unclear due to the study's limited design, which lacked control groups and repeated measurements, their work highlights the potential of search-based advertisements as a strategy to reach individuals at high risk of suicide.

It should be noted here that one of the papers listed above (Kashihara et al., 2025) was coauthored by the three guest editors of this special issue. To ensure a fair and unbiased review process, Dr. Tetsuya Yamamoto (Tokushima University) served as the action editor for this paper. We sincerely appreciate his careful and professional handling of this manuscript, which has made a valuable contribution to the issue. We also extend our gratitude to the anonymous reviewers whose insightful comments considerably helped the authors to improve the quality of their papers through revisions and also helped the editors to make well-reasoned decisions on the submitted manuscripts. Further gratitude is extended to the authors of the rejected studies. Many of these papers boldly employed novel techniques and perspectives originating from fields outside traditional clinical psychology research. We greatly appreciate the innovative spirit of these authors and hope they will continue to refine and expand their unique projects to meet the publication standards for psychological research.

Thanks to the tremendous contributions of the professionals mentioned above, the special issue titled “Innovations in Clinical Psychological Science in the Era of Complexity” has been successfully published. However, we dare to pause here to raise critical questions about our own issue. Can this issue be regarded as a product of full-fledged clinical psychological science in Japan? Are the papers included in this issue innovative enough compared to the influential papers garnering worldwide attention in our field? We editors do not think so. Every study included in this special issue has many more steps ahead to create genuine innovations. The authors of the review papers on the psychological network approach (Kashihara et al., 2025) and NIRS (Iwayama et al., 2025) must conduct empirical studies aligned with their visions to demonstrate the clinical utility of their specialized methodologies. The complex system models of depression examined by Omizu and Kunisato (2025) should be refined through iterative cycles of theory, phenomena, and data (see Borsboom, van der Maas, et al., 2021, for more details) to develop a robust formal theory, as Robinaugh et al. (2024) accomplished in their research on panic disorder. Descriptive studies on the use of wearable trackers to measure sleep characteristics (Uchida & Kurosawa, 2025) and the pilot trial of search-based advertisements for suicide prevention (Ono et al., 2025) must be followed by research guided by clearly defined questions that remain unanswered in these areas. The exploratory use of machine learning approaches (Takeshige et al., 2025) requires the development of a realistic and cost-effective strategy to improve the performance of predictive models to achieve the ambitious goal of detecting depression on mobile devices. Whether these current studies can lead to future genuine innovations in their research areas now depends heavily on the ongoing efforts of the authors.

In addition to these questions, we pose further questions for Japanese researchers in clinical psychology. Is clinical psychology in our country sufficiently mature to become a hub of the hub science (APS, 2020)? Are we prepared to embrace the novel ideas emerging at the frontiers of interdisciplinary science? We editors do not think so. While handling this special issue, we observed that both authors and reviewers encountered difficulties in fully explaining and understanding the cutting-edge perspectives and methodologies imported from the outside of traditional clinical psychology. Given that most interdisciplinary approaches are initially introduced in incomplete forms, both authors and reviewers should remain open to failure and cultivate an attitude to enjoy trial and error. It is equally important to return to the fundamentals of the peer review process. We hope that more authors will continue developing their academic writing skills and avoid overstating their findings. Well-structured, straightforward, and logical papers are more likely to receive constructive feedback from reviewers and readers. Believe in your research as it is, regardless of its level of sophistication. We also hope that more reviewers focus on providing constructive feedback, as achieved by the anonymous reviews for this special issue, rather than forcing themselves to understand every detail in the submitted papers. Even if parts of a study use unfamiliar methodologies, reviewers still have opportunities to assist authors by providing detailed feedback to improve the quality of writing. Well-structured, fair, and constructive comments—including those suggesting the rejection of papers—can help authors mature their novel approaches. Believe in your fundamental academic skills and behave as you are. Such a fair and straightforward culture can considerably enhance the peer review process and help both authors and reviewers embrace and enjoy interdisciplinary approaches. We hope that this special issue serves as a stepping-stone for transforming Japanese clinical psychology into interdisciplinary clinical psychological science and that future researchers will create genuine innovations to advance our field.

The authors declare no conflicts of interest associated with this manuscript.

This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant numbers: 21H05064 and 21H05068) and the Toyo University Inoue Enryo Memorial Grant (grant number: 705).