Cyberpsychology, behavior and social networking最新文献

A previous study has found a negative correlation between meeting one's romantic partner in online dating (vs. offline) and marriage quality through a quota sample of the U.S. population. This current study replicates and extends this previous study by analyzing another sample of the U.S. population from the Pew Research Center (N = 2,787). Largely consistent with the previous finding, we found that meeting one's partner in online dating (vs. offline) was marginally related to experiencing less relationship success among people in a marital relationship. However, the difference was not observed among people in a nonmarital romantic relationship. In addition, sharing or discussing one's relationship or dating life (i.e., relationship talks) on social media (RToSM) conditioned the link between meeting venue and relationship success. That is, for marital relationships, meeting in online dating was only linked to less relationship success among people with no RToSM but not among those who had RToSM; for nonmarital relationships, there was a positive link between meeting in online dating and relationship success among those who had RToSM, but this positive link was not observed among those who had no RToSM. We further discuss the implications of our findings on the long-term impact of online dating in marital and nonmarital romantic relationships.

Recent studies have indicated the potential detrimental psychological effects of Instagram use (IU) and problematic Instagram use (PIU). PIU and IU have been associated with depression, anxiety, and negative general well-being. This study aimed to investigate that correlation through a systematic review and meta-analysis. Seven databases, including PubMed/MEDLINE, were explored in February of 2023. Studies that assessed the association between IU and/or PIU with depression, anxiety, other psychological distresses, and/or general well-being were deemed eligible. The Hunter-Schmidt random effects model and multilevel meta-analysis were applied to assess the outcomes of interest. A total of 1,927 hits were identified with 37 studies included in the quantitative analysis after removal of duplicates and screening of abstracts and full texts, with the total number of participants at 14,305. The obtained correlations between PIU and depression, anxiety, and well-being with 95% confidence interval were found to be r = 0.35, [0.25, 0.44], r = 0.31, [0.22, 0.40], r = -0.17, [-0.24, -0.10], respectively. Similarly, for IU and depression r = 0.11, [0.06, 0.16], p < 0.05 for all of the aforementioned results. The association between IU and anxiety was found to be r = 0.12, [0.04, 0.21], p < 0.05. The present meta-analysis found significant conceptual heterogeneity across the studies included due to different study designs and population heterogeneity. The results show a positive association of PIU and IU with depression, anxiety, and other psychological distress(es). There is a need for robust designed longitudinal research to explore this association.

Internet and Communication Technologies (ICTs) can foster efficient communication and knowledge acquisition, but there are also tradeoffs in terms of risks to one's privacy. Previous research, including work with the privacy calculus framework, indicates that factors such as perceived risks and benefits of using ICTs, ICT trust, and general privacy concerns can influence individuals' digital privacy-related decisions. One pervasive psychological factor that may potentially alter such privacy-related behaviors is acute stress. Acute stress can promote risk-seeking behaviors and a tendency to prefer immediate rewards over delayed, greater value rewards. However, the effect of acute stress in the applied context of privacy decision making is relatively unknown. Participants (N = 143) in this study were randomly assigned to either an acute stress task (socially evaluated cold pressor task) or an active control task (lukewarm water alone). Results revealed that acute stress condition increased information disclosure, as indexed by accepting more online cookies, sharing one's location more frequently, and revealing greater willingness to self-disclose personal information. In addition, the impact of individuals' levels of perceived risk and benefits, trust, and privacy concern on privacy decision making was examined. However, none of these constructs consistently influenced privacy decisions over and above the effect of stress. Overall, our findings suggest that acute stress has robust, independent influence on privacy decision making.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

Deficits in executive functions (EF) are strongly related to real-life functioning and negative symptoms (NS) in schizophrenia. Recently, virtual reality has enabled more ecologically valid approaches to assess EF in simulated "real-life" scenarios among which the virtual cooking task (VCT) has gained attention. However, the clinical implications of the VCT in schizophrenia have not been investigated exhaustively. In this study, clinically stable individuals with schizophrenia (n = 38) and healthy controls (n = 42) completed a novel VCT and a set of computerized standard EF tools (CST) to primarily investigate concurrent and discriminant validity. In addition, the study explored links between EF assessments, functioning, and NS while controlling for antipsychotic intake, clinical stability, and age. This VCT consisted of four tasks with increasing difficulty and time constraints. The most relevant findings indicate that (1) the VCT showed moderate to strong correlations with CST, (2) the VCT discriminated EF performance between both the groups, (3) the VCT predicted interpersonal functioning, and (4) the VCT predicted NS in greater extent than CST. Accordingly, the findings give support to the concurrent and discriminant validity of the VCT to assess EF and indicate its value to deepen the study of collateral functional deficits and NS in schizophrenia.

During the COVID-19 pandemic, the extensive lockdown measures implemented for disease mitigation triggered a surge in round-the-clock social media use, giving rise to widespread concerns regarding its impact on sleep health. This meta-analysis examined the association between social media use and sleep disturbance during the pandemic, along with potential moderators. The dataset included 43 independent samples comprising 68,247 residents of 21 countries across 7 world regions. The three-level mixed-effects meta-analysis revealed a weak, positive overall effect size (r = 0.1296, 95% confidence interval: 0.0764-0.1828, k = 90). The magnitude of the effect size varied by the type of social media use: compulsive use exhibited a moderately strong effect size, whereas information-focused use showed marginal significance. The effect size was more pronounced in countries imposing stricter (vs. less strict) lockdown measures. Lockdown status also moderated this association, with a marginally significant effect size observed during lockdowns but a significant effect size after lockdowns. For demographics, samples involving emerging adults demonstrated moderately strong effect sizes, whereas those involving the general population had modest effect sizes. Notably, the interaction between the type of social media use and lockdown status was significant. Specifically, the positive association with information-focused use was significant only during lockdowns, whereas that with general use was significant after, but not during, lockdowns. However, compulsive use showed a moderately strong effect size both during and after lockdowns. These findings underscored the importance of considering multiple factors-such as the type of social media use, context, and demographics-when studying social media use and sleep health.