Japanese Psychological Research最新文献

The two dimensions of adult attachment orientations, namely, anxiety and avoidance, are known to be associated with how individuals regulate their emotions. However, little is known about how attachment orientations influence emotional interdependence—the associations between one's previous emotions and another's present emotions. This study investigated the moderating effect of attachment orientations on this dynamic interdependent process. Data were drawn from an experience sampling study of 54 cohabiting heterosexual Japanese couples, who reported positive and negative emotions and the presence of partners four times a day for 14 days. Dynamic structural equation modeling showed that emotional interdependence was significant between men's previous positive emotions and women's present positive and negative emotions. Furthermore, women's attachment avoidance was negatively associated with emotional interdependence between their previous negative emotions and men's present negative emotions. These effects remained significant regardless of whether the partners were physically together. Although the limited statistical power restricts the strength of our conclusions, these results provide preliminary support for the role of the deactivating strategy linked to attachment avoidance, suggesting that women with high attachment avoidance suppress their expression of negative emotions that were not adequately perceived and shared by their partners.

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 methodologie

This study aims to describe the pilot operation of upstream approaches for suicide prevention using search-based advertising. Outreach was conducted to Internet users who searched for keywords related to the six life issues with risk factors for suicide (perinatal women, domestic violence, addiction, depression, sexual minorities, and child abuse). Search-based advertisements were displayed 1,481,025 times and clicked on 62,884 times (click rate: 4.25%). Since the mental health status of those in the five areas of life issues, except for addiction, was considered serious, outreach using search-based advertising could expedite effective support information to those with serious mental health conditions. In the addiction area, outreach to those with gambling and alcohol addiction was considered effective, but effective outreach to those with drug addiction was found to be difficult within the framework of this study. In conclusion, this study shows that upstream approaches for suicide prevention using search-based advertising can reach Internet users with specific life issues and deteriorating mental health.

Self-report questionnaires, used for detecting major depressive disorder (MDD) in daily life, may incur biases stemming from social desirability and repetitive answers. Though detection based on mobile sensing was being developed recently, it cannot sufficiently promote self-help action due to the characteristics of passive feedback. Thus, an active self-monitoring and feedback system is crucial for individuals to recognize and address their malfunctions. In this study, we proposed to predict changes in MDD severity using cognitive tasks monitored on mobile devices. An online survey was conducted to evaluate the severity, incorporating cognitive tasks such as Navon task, Go/No-go task, and n-back task, along with the Quick Inventory of Depressive Symptomatology. Participants completed the survey three times on their mobile devices. The analysis included data from 75 participants, including 21 participants whose MDD score increased by at least one point during the second and third surveys; the first survey was excluded to avoid confounding effects. A random forest classifier was employed for classifying participants whose depression has and has not worsened. The learned model achieved modest accuracy (68.3%) with a significant mean area under the curve of 0.59 (t(9) = 2.98, p = .016, d_z = 0.94), suggesting the potential to predict depressive states based on cognitive domains. Moreover, working memory and attentional inhibition functions contributed to predicting the severity change mostly. Though improvements are required to reduce false negatives for practical applications, our result suggests that MDD aggravation could be assessed by mobile cognitive tasks.

The transition from high school to university is characterized by changes in students' social and learning environments, potentially impacting sleep and learning behaviors. To examine the sleep characteristics of university students, this study aimed to (a) measure sleep in a living environment using a wearable tracker; (b) use a self-administered questionnaire to examine the relationship between objective and subjective sleep; and (c) examine how psychological aspects are related to objective and subjective sleep. Fifty undergraduate and graduate students participated, with 310 sleep records obtained. The objective means total sleep time showed a moderately positive correlation with sleep quantity in the 3-Dimensional Sleep Scale. Furthermore, we found that mental health, trait anxiety, and quality of life do not correlate with objective total sleep time but are significantly moderately correlated with subjective sleep quantity. The combination of wearable trackers and subjective reports may provide a more precise understanding of university students' actual sleep patterns and how different aspects of sleep behavior are associated with health outcomes.

In recent years, near-infrared spectroscopy (NIRS) has garnered significant attention for its potential clinical applications as a non-invasive and straightforward method for measuring brain functions. While NIRS has become useful in psychiatric contexts by aiding in the differential diagnosis of depression, its application in clinical psychology is still developing. Due to its ecological validity, which allows for measurements in natural settings, NIRS shows promise in revealing the dynamic elements of the psychotherapy process. In this study, we conducted a scoping literature review of the use of NIRS in psychotherapeutic interactions, exploring its potential to unveil the complexities of psychotherapy settings involving interpersonal interactions. Employing a scoping review approach, we systematically searched the PubMed, PsycINFO, and Web of Science databases for articles published through October 19, 2023 about studies using NIRS as a measurement instrument: This yielded various therapeutic interactions. Of the 155 retrieved results that remained after the initial screening, seven met the inclusion criteria. Of these, six studies examined the synchronization of brain functions through the simultaneous brain activity measurements (hyperscanning) of two participants. The review revealed that, despite the anticipated potential of NIRS, it has become evident there is a dearth of research focusing on interactions during psychotherapy. Furthermore, the few available studies have not explored changes in line with the progression of psychotherapeutic interactions. Consequently, future studies should address whether NIRS can effectively assess alterations in brain function resulting from micro-level events, such as the therapeutic interaction between therapists and clients during psychotherapy.

We conducted mathematical simulations to identify effective treatment options for depression. These simulations involved treatment component nodes that targeted specific symptoms within the depressive symptom network, examining how interventions for certain symptoms could improve the overall symptomatology. The results indicate that targeting a symptom with higher centrality can lead to significant improvement. Furthermore, while interventions targeting symptoms with the lowest centrality did not show any improvement, targeting multiple lower-centrality symptoms simultaneously may lead to an improvement in overall symptomatology. Insights from these simulations are expected to advance the understanding of effective intervention strategies for depression and the application of psychological networks in clinical settings.

With the advancement of AI-generated human motion, it is of increasing importance to think about how we distinguish real human motion from machine-generated movements. In this study, we recruited professional performers to use the whole body to make a short movement to inform potential observers that they are real humans (instead of machines). Their movements were captured with a motion capture system (Vicon) and later reduced to dynamic point-like displays (biological motion). They were interviewed after the recording to provide their acting strategies. Naive observers who did not participate in the motion data collection were recruited to watch these videos and judge whether the biological motions looked human-like or not (YES/NO), as well as to report their judging criteria. The major factors extracted from these reports include kinematics, context, body mechanics, and principles of physical laws. We discuss the impact of these criteria and how they may possibly help improve the future generation of human-like motions.