Cyberpsychology, behavior and social networking最新文献

While individuals exhibit heightened attentional bias toward social media information, it remains debated whether this reflects a content-specific priority or a broader cognitive adaptation. Using visual search tasks, self-report scales, and the Embedded Figures Test, this study investigated attentional capture by digital icons and the role of cognitive style. Results showed that icons, particularly for field-independent individuals, elicited greater capture, suggesting a generalized digital attentional bias, and cognitive style play a key role in the process.

This study was performed to clarify the relationship between smartphone usage and depression risk among older adults. We also examined smartphone use variations, population heterogeneity, and the mechanisms behind smartphone-related depression symptoms. A total of 10,997 older adults (mean age = 84.31 years) from a national cohort (Chinese Longitudinal Healthy Longevity and Happy Family Study) in China were included. Smartphone use was measured by use status (yes/no) and contents. Among eight dichotomous questions about contents of smartphones, chatting, shopping, traveling, and entertainment were classified as routine-oriented activities. In contrast, health management, financial management, and learning were classified as functional/cognitive-oriented activities. Thus, participants were categorized into four groups, including not using a smartphone, using smartphones both for routine-oriented and functional/cognitive-oriented activities, only for routine-oriented activities, and others. Smartphone users had lower 10-item Center for Epidemiological Studies Depression Scale scores (β = -0.62; p < 0.001) and a reduced depression risk (odds ratio = 0.77; p < 0.001) compared with nonusers. Furthermore, those using smartphones for routine-oriented and functional/cognitive-oriented activities had even lower risks than those using them only for routine-oriented activities. Second, using smartphones was more sensitive to alleviating depression among older adults aged <80 years or living in rural areas. Third, mediation analyses suggested that using smartphones may be associated with depressive symptoms through communicating with children regularly. Our findings suggest that smartphone use may have a positive effect on mental health, particularly among those who engage in diverse routine-oriented and functional/cognitive-oriented activities. Smartphone benefits are more evident in younger seniors and rural residents, indicating a need for targeted interventions for these groups.

While the effects of interpersonal social comparison have been widely explored, the potential consequences of human-machine comparison remain understudied. The present survey research investigates how individuals compare themselves to generative artificial intelligence (GAI), by extending the concept of social comparison orientation to human-GAI interactions. Using a large Chinese sample (N = 1302), we examine the relationships between two subtypes of human-GAI comparison orientation (ability-based and opinion-based), self-esteem, perceived identity threat, anthropocentric beliefs, and personal relative deprivation within the framework of the Rejection-Identification Model. The findings reveal that both ability-based and opinion-based comparisons with GAI positively relate to self-esteem, diverging from relevant findings on interpersonal comparisons. In addition, ability-based comparisons with GAI significantly heighten the perceived identity threat, whereas opinion-based comparisons with GAI may alleviate it. Furthermore, anthropocentric beliefs (i.e., human-first attitudes) suppress the negative effect of identity threat on self-esteem, which is also mediated by personal relative deprivation. In addition, a post hoc exploratory multigroup analysis was conducted based on usage frequency. The present study highlights the importance of investigating human-AI comparison and expands the explanatory power of classical concepts and frameworks from the self and identity domains, contributing to psychological interventions aimed at mitigating the potential adverse effects driven by human-machine relationships.