Fan Yang

Brief campus-based interventions for university students with elevated internet gaming disorder (IGD) symptoms have received little study. This randomized trial evaluated a five-session group program focused on gaming motives, self-regulation, and alternative sources of competence and self-worth. Sixty-three adult students at a Chinese vocational college who scored at least 20 on an adapted Gaming Disorder Scale for Adolescents were randomized to the program (n = 31) or an information-only control condition (n = 32). Assessments were scheduled at baseline, immediately after the intervention, and one month later. Because post-randomization outcome data were unavailable for 19 participants, the comparative analyses were restricted to 44 completers. The originally planned baseline-adjusted ANCOVA showed a lower adjusted T1 IGD score in the intervention group than in the control group (adjusted difference = −4.62, 95% CI −7.33 to −1.91, p = 0.0013). In a post hoc linear mixed-effects model, the between-group difference in change from baseline to post-intervention was −5.16 points (95% CI −7.81 to −2.51; Hedges g = −1.03). At one month, the difference was smaller and not statistically significant (−1.67 points, 95% CI −4.31 to 0.98). None of the secondary outcomes showed a significant interaction. Overall attrition was 30.2%. These preliminary findings concern selected completers rather than an intention-to-treat effect, and the between-group difference was not evident at one month. Retrospective registration, unavailable outcomes for non-completers, and post hoc analyses limit the strength of the inference. The trial was retrospectively registered in UMIN-CTR (UMIN000055209) on 9 August 2024.

Generative artificial intelligence (AI) is increasingly used as a conversational partner, yet little is known about when AI‐generated impressions of users converge with users' self‐views. Drawing on lens theory, attachment theory and the literature on belongingness and social connection, the current study examined whether users' emotional bonds with AI moderate self‐AI agreement in personality reports. A total of 738 Japanese ChatGPT users completed self‐report measures of the Big Five personality traits and AI attachment dimensions. Their personality was also inferred by ChatGPT based on their chat history. Self‐reported and AI‐inferred Big Five traits were positively correlated, although AI systematically rated users higher across all five domains. Moderation analyses showed that AI attachment anxiety, but not avoidance, strengthened self‐AI agreement for extraversion, agreeableness and negative emotionality. Supplemental sensitivity analyses, including all participants who provided AI‐inferred personality data ( N = 789), produced the same conclusions regarding positive self‐AI agreement, AI overestimation and the absence of moderation by AI attachment avoidance, while indicating that the attachment anxiety‐based moderation was most robust for negative emotionality. These findings suggest that AI‐based personality inference is shaped not only by model capability but also by the relational cues users provide in human–AI interaction.

The current study examined whether imagining a future secure relationship can increase state attachment security to a degree comparable to recalling a secure past or current relationship. In a within‐subjects design, 88 participants completed three weekly tasks: future thinking, episodic recall, and a neutral shopping control. Each session involved a brief imagery‐and‐writing task followed immediately by state attachment measures. Compared with the neutral control, both relational priming tasks were associated with higher state attachment security, and the future and recall tasks did not differ. Effects on attachment anxiety and avoidance were small and did not reach conventional levels of statistical significance after correction. These findings provide preliminary evidence that episodic future thinking can prime felt security in the short term, while also indicating that stronger evidence is needed before drawing conclusions about functional or intervention‐related significance.

What we imagine about the future can shape our present emotions, decisions, and behaviors. A key question, then, is whether individuals spontaneously think about their interpersonal relationships when engaging in episodic future thinking. Two studies explored the influence of attachment style on episodic future thinking content. Study 1 involved 155 participants conducting five episodic future thinking tasks. Results show that attachment anxiety positively predicted the relevance of the content to interpersonal relationships, whereas attachment avoidance negatively predicted this relevance. Study 2 involved 67 participants performing nonstress and stressful episodic future thinking. Results showed attachment anxiety consistently increased interpersonal content, while attachment avoidance reduced it only in nonstress tasks. These findings support the features of attachment style also manifest when imagining future scenarios.

Attachment theory is one of the key frameworks for understanding close relationships, yet how marital attachment interacts within a dyadic context remains unclear. Using two‐wave longitudinal data with a 6‐month interval collected from 501 heterosexual spousal pairs, the present study examined interactive relationships in marital attachment within a dyadic context through cross‐lagged panel network (CLPN) analysis. Firstly, the results found that wives' attachment components more strongly predicted husbands' attachment components within CLPN. Secondly, wives' attachment avoidance component, not show feel deep down , more strongly predicted other components and husbands' components within CLPN. Finally, bidirectional associations were observed between wives' and husbands' attachment avoidance components. These findings deepen our understanding of marital attachment interactions and offer practical insights for marital and family practices aimed at improving marital relationships.

Adolescence marks an important stage in attachment development, during which parental parenting plays a significant role in shaping adolescent outcomes. This study explored the bidirectional relationship between parental overparenting and adolescents' attachment style. To this end, we used a two‐wave cross‐lagged model over a 6‐month interval with a sample of primary and middle school students ( N = 1897) from Sichuan Province, China. Results showed that maternal and paternal overparenting positively predicted both attachment anxiety and avoidance. Adolescents' attachment anxiety and avoidance also predicted parental overparenting. In addition, paternal overparenting did not predict maternal attachment style, and maternal overparenting did not predict paternal attachment style. These findings indicated that overparenting may link to the reinforcement of adolescents' attachment styles, which may in turn encourage further overparenting. The results of this study not only deepened our understanding of attachment theory but also provideed guidance for optimising family educational practices.

Attachment style is related to general self-efficacy. However, the underpinning mechanisms regarding this association remain unclear. The way people treat their own experiences is vital to general self-efficacy. Mindfulness is a key feature of attachment security, and mindful individuals could utilize positive experiences efficiently and prevent negative ones from impacting their self-efficacy. Thus, the current study investigated the potential mediating role of mindfulness in the association between attachment style and general self-efficacy. Study 1 used a cross-sectional design with 709 participants living in Tokyo, Japan. The results of Study 1 indicated that mindfulness was negatively associated with attachment avoidance and anxiety and positively associated with general self-efficacy. Attachment avoidance and anxiety were negatively related to general self-efficacy, and mindfulness mediated this relationship. Study 2 adopted a cross-lagged model to examine the directionality of these variables. Data from 408 Japanese participants showed that, at a 3-month interval, attachment anxiety negatively predicted mindfulness and general self-efficacy, and mindfulness negatively predicted general self-efficacy. Attachment avoidance positively predicted mindfulness, and mindfulness negatively predicted attachment avoidance. These findings support that evaluative attitude and the amplified negative emotions of attachment anxiety may constitute an unstable inner environment, which is likely to hamper mindfulness and general self-efficacy.

Artificial intelligence (AI) is growing “stronger and wiser,” leading to increasingly frequent and varied human-AI interactions. This trend is expected to continue. Existing research has primarily focused on trust and companionship in human-AI relationships, but little is known about whether attachment-related functions and experiences could also be applied to this relationship. In two pilot studies and one formal study, the current project first explored using attachment theory to examine human-AI relationships. Initially, we hypothesized that interactions with generative AI mimic attachment-related functions, which we tested in Pilot Study 1. Subsequently, we posited that experiences in human-AI relationships could be conceptualized via two attachment dimensions, attachment anxiety and avoidance, which are similar to traditional interpersonal dynamics. To this end, in Pilot Study 2, a self-report scale, the Experiences in Human-AI Relationships Scale, was developed. Further, we tested its reliability and validity in a formal study. Overall, the findings suggest that attachment theory significantly contributes to understanding the dynamics of human-AI interactions. Specifically, attachment anxiety toward AI is characterized by a significant need for emotional reassurance from AI and a fear of receiving inadequate responses. Conversely, attachment avoidance involves discomfort with closeness and a preference for maintaining emotional distance from AI. This implies the potential existence of shared structures underlying the experiences generated from interactions, including those with other humans, pets, or AI. These patterns reveal similarities with human and pet relationships, suggesting common structural foundations. Future research should examine how these attachment styles function across different relational contexts.

Background: Previous research has found that attachment style is associated with self-concept clarity, and mindfulness mediates this association at an individual level. However, there is currently a lack of research on the relationship between attachment style, mindfulness, and self-concept clarity from an interpersonal perspective. Therefore, the current study aimed to extend the relationship between attachment style and self-concept clarity to a dyadic context.Participants and procedure: To this end, we recruited online a final sample of 773 pairs of Chinese heterosexual couples (Mage = 35.43, SDage = 3.77 for wives; Mage = 37.46, SDage = 4.39 for husbands) to measure their attachment style, mindfulness, and self-concept clarity and conducted actor-partner interdependence mediation modeling.Results: The results indicate actor effects whereby an individual’s mindfulness significantly mediated the relationship between their attachment style (both avoidance and anxiety) and self-concept clarity for both women and men. Furthermore, as regards the partner effect, the mindfulness of men significantly mediated the relationship between the attachment anxiety of women and the self-concept clarity of men.Conclusions: Altogether, we provide evidence that the impact of attachment style on self-concept clarity within a dyadic context and the level of husbands’ mindfulness could partly explain the relationship between wives’ attachment anxiety and husbands’ self-concept clarity. Implications of these findings are discussed.