Cyberpsychology,Behavior,and Social Networking

The dual (bidirectional) nature of social media suggests that fear-of-missing-out (FOMO) leads to greater social media use (SMU). In turn, higher levels of SMU lead to heightened FOMO. Ironically, individuals use social media to assuage their FOMO but end up with higher levels of FOMO after being exposed to a wide variety of social opportunities, where they may not have been included. The present research examines the hypothesized bidirectional causal flow between FOMO and SMU. Extant research involving FOMO has been largely correlational. In Study 1, FOMO was manipulated and found to increase reported levels of SMU. Study 2 manipulated SMU, which led to higher levels of FOMO. It appears that, regarding FOMO, social media does exhibit a dual (bidirectional) nature.

There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In study 1, we explore variation within older adults' experiences with VR through a field study of VR use among a large (N = 245) and age-diverse (M_age = 83.6 years, SD_age = 7.9, range = 65–103 years) sample of nursing home and assisted living facility residents across 10 U.S. states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults' attitudes toward VR was significantly less negative than the relationship between age and their attitudes toward other technologies (cell phones and voice assistants). In study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.

Predicting treatment response can inform treatment decisions, expectations, and optimize use of mental health treatment resources. This study examined heart rate (HR), heart rate variability (HRV), and a modified Stroop task (mStroop) to predict post-traumatic stress disorder (PTSD) treatment response. We report on an observational, longitudinal study with 45 U.S. veterans in outpatient PTSD care, who had deployed to Iraq or Afghanistan. HR and HRV were collected before, during, and after virtual reality (VR) combat and civilian scenes. HRV recovery was defined as HRV after a 3-minute VR simulation minus HRV during a VR scene. mStroop threat variables included index scores for combat and general threat. Self-report data were collected at baseline and 6 months later. The outcome variable was the 17-item Clinician Administered PTSD Scale (CAPS). Controlling for baseline CAPS and number of combat experiences, the following baseline HRV recovery variables were significant predictors of 6-month CAPS: standard deviation of normal beat to beat interval (SDNN) after combat scene minus SDNN during combat scene and low-frequency (LF HRV) after civilian scene minus LF during civilian scene. HRV at rest, HR reactivity, HR recovery, and mStroop scores did not predict treatment response. In conclusion, HRV recovery variables in the context of a standardized VR stressor were significant predictors of PTSD treatment response after controlling for baseline CAPS and number of combat experiences. The direction of this relationship indicates that greater baseline HRV recovery predicts lower 6-month PTSD symptom severity. This was an exploratory study in need of replication.

There has been limited examination of the phenomenon of the victim-offender overlap in the field of technology-facilitated abuse (TFA). To design effective strategies to prevent TFA, it is important to understand which individuals are most at risk of victimization, perpetration, and to what extent a subset of people both experience victimization and engage in perpetration. This study drew on Cyber-Abuse Research Initiative (CARI) data, a nationally representative U.S. sample of adults ages 18–35. TFA measurement consisted of parallel scales for victimization and perpetration, each with 27 items assessing forms of technology-facilitated surveillance, monitoring/tracking, interference/communications, reputational harm, controlling/limiting access, and fraud. A bivariate probit of TFA perpetration and TFA victimization, as separate outcomes, was fit to allow for joint estimation of regression coefficients and robust standard errors. Analyses confirmed that TFA, similar to other forms of interpersonal aggression, is characterized by a substantial victim-offender overlap, with 30 percent of the sample reporting involvement both as a victim and as a perpetrator. Internet/social media use and social isolation did not distinguish victimization and perpetration. However, positive and negative affect as well as Lesbian, Gay, Bisexual, Queer, Asexual, or other sexual orientation (LGBQA+) were positively correlated with victimization, whereas female gender and having postsecondary education were positively associated with perpetration. These results may be used to design interventions and anticipate service needs. TFA, as a new topic of research, should capitalize on the theoretical and empirical article related to other forms of the victim-offender overlap.

The introduction of chat generative pretrained transformer (ChatGPT), the fastest growing large language model, has changed the landscape of artificial intelligence–human interaction in everyday life. As the social influence of ChatGPT increases, competencies in it become important life skills. This study aims to explore the determinants of ChatGPT user satisfaction to provide practical implications by suggesting a significant independent variable and mediators between the independent variable and user satisfaction. To this end, this study recruited 822 college students with prior experience using ChatGPT (407 males and 415 females) and conducted an online survey. We tested the effects of ChatGPT literacy on user satisfaction and the mediating roles of different motives (i.e., information and knowledge acquisition and entertainment and leisure) in the relationship between ChatGPT literacy and user satisfaction. The results suggest that ChatGPT literacy significantly increases user satisfaction and that information and knowledge acquisition and entertainment and leisure partially mediate the relationship between the effect of ChatGPT literacy and user satisfaction. The results may have implications for large language model developers and practitioners, such as educators.

This study pragmatically investigates an artificial intelligence (AI) speaker (AIS)'s verbal communicative performance based on real AI–human conversation data. Specifically, this study explores Grice's conversation theory, which enables the categorization of an AIS's mistaken utterances as violations of specific conversational maxims. Twenty native Korean-speaking participants recorded at least 50 conversations with Kakao Mini AISs, provided by Daum Kakao, Inc., in Korea. Each conversation, either for information sharing or as daily dialogue, was required to contain at least two turn-taking instances. A total of 1,026 recorded dialogues were decomposed into adjacency pairs based on turn-taking. The dialogues were arranged into 3,365 adjacency pairs, and each pair was then classified as a conversational success or failure based on whether the AIS answered the user's utterance appropriately. Language users' evaluations of the AIS's mistaken expressions were also quantified via an additional acceptability rating test with 1,024 adjacency pairs. The overall results indicate that Grice's “maxim of relation” is most frequently flouted by AISs and is considered to be the least natural to language users. These findings suggest that to improve AISs' natural communication capacity, more detailed AI algorithms that generate utterances relevant to either the partner's preceding utterance or a broader conversational context should be created. Although the verbal communicative capacities of the AIS we test are substantially overtaken by those of recent large language models, such as generative pretrained transformer, the pragmatic evaluation described in the current study will remain useful for more precise linguistic quantification of current/future language AI's communicative performance/competence.

Amblyopia affects development of children's monocular vision and binocular function and becomes a largely intractable problem with increasing aging. This study is to investigate the binocular function and evaluate efficacy of digital therapy in children 8–13 years of age with anisometropic amblyopia. The patients in the digital therapy group performed the training with the digital amblyopia therapeutic software. The visual acuity and binocular function (perceptual eye position [PEP], suppression, and stereopsis) were examined at the first visit and 3-month post-treatment. Twenty-three cases in the control group and 25 cases in the digital therapy group were enrolled. The results revealed that 3-month digital therapy can effectively improve corrected distance visual acuity (CDVA) and improve the binocular function, including PEP, suppression, and second-order stereopsis in children with anisometropic amblyopia, 8–13 years of age. Digital therapy for amblyopia can effectively improve monocular CDVA of amblyopic eyes and binocular function in older children with anisometropic amblyopia.