Cyberpsychology, behavior and social networking最新文献

Romantic rejections are a hurtful yet common occurrence in online dating. While research in this area is growing, there is a need for a comprehensive and comparative overview to understand these rejection experiences better. This article presents the results of two cross-sectional survey studies that aimed to create a more comprehensive overview of multiple facets of rejections in online dating, particularly the types of rejections used, the (provided) reasons for rejecting, and the painfulness of being rejected. Results of Study 1 (n = 177) show that ghosting was the most often experienced rejection type, even when a considerable number of messages was exchanged before the rejection. Unmatching/blocking and rejections with an explanation occurred less but equally often. Moreover, individuals who experienced rejections with an explanation reported the highest painfulness rates, which raised important questions about the reasons behind such explicit rejections. Study 2 further unpacked the provided rejection reasons, from the rejecter and the rejectee perspective. Five categories were identified among the reasons, such as (lack of) attraction and reasons related to relationship investment. Several interesting discrepancies that emerged between reasons provided by rejecters versus rejectees are discussed. Our work underlines the multifaceted nature of rejection experiences in online dating and sets directions for future research that further explores the relationships between rejection types, reasons, and painfulness in detail.

Human moral reactions to artificial intelligence (AI) agents' behavior constitute an important aspect of modern-day human-AI relationships. Although previous studies have mainly focused on autonomy ethics, this study investigates how individuals judge AI agents' violations of community ethics (including betrayals and subversions) compared with human violations. Participants' behavioral responses, event-related potentials (ERPs), and individual differences were assessed. Behavioral findings reveal that participants rated AI agents' community-violating actions less morally negative than human transgressions, possibly because AI agents are commonly perceived as having less agency than human adults. The ERP N1 component showed the same pattern with moral rating scores, indicating the modulation effect of human-AI differences on initial moral intuitions. Moreover, the level of social withdrawal correlated with a smaller N1 in the human condition but not in the AI condition. The N2 and P2 components were sensitive to the difference between the loyalty/betrayal and authority/subversion domains but not human/AI differences. Individual levels of moral sense and autistic traits also influenced behavioral data, especially on the loyalty/betrayal domain. In our opinion, these findings offer insights for predicting moral responses to AI agents and guiding ethical AI development aligned with human moral values.

It is well known that social interaction enhances learning processes, improving abilities such as attention and memorization. However, it is not clear whether similar advantages may be obtained even in virtual environments. Here, we investigate whether virtual interactions in a video game, similarly to real-life social interactions, may improve individuals' performance in a subsequent implicit learning task. Twenty-one healthy participants were asked to play a cooperative video game for 20 minutes in three different gaming modalities: alone (Single-Player); together with someone without verbal interactions (Multi-Player -); and with someone with verbal interactions (Multi-Player +). After each gaming session, participants were presented with an EEG paradigm directed to measure mismatch negativity (MMN) responses, a well-validated index of implicit learning. MMN responses were significantly larger following Multi-Player +, as compared with Single-Player, and Multi-Player - conditions. No significant difference was found between Single-Player, and Multi-Player - conditions. These results indicate that implicit learning processes are enhanced following communicative virtual interactions. Verbal interaction in a virtual environment seems necessary to elicit social copresence and its positive effects on learning performances. This finding may have important implications for the design of virtual rehabilitation protocols and distance learning programs.

The global shift to remote work during the COVID-19 pandemic has intensified scholarly attention to remote workers' well-being. Although existing studies explore the varied impacts of remote work, there is a gap in understanding remote workers' well-being through the lenses of social disparity and the digital divide. Extending digital divide scholarship to the remote work context, this study disentangles why some remote workers experience better well-being than others. We conducted a two-wave longitudinal panel study in South Korea during the COVID-19 pandemic (Wave 1: February 2021, Wave 2: October 2021). Among the 501 participants who participated in both waves, we found that individuals with lower education levels were less likely to have remote work opportunities. We focused our further analyses on a subset of 144 employees who had remote work opportunities within organizations with typical hierarchical structures. We found that socioeconomic status (SES) did not directly influence remote workers' well-being but indirectly influenced it by contributing to the diversity in using information and communication technologies (ICTs). Workers with higher SES or more diversity in using ICTs demonstrated lower vulnerability and more effectiveness in maintaining their well-being in virtual organizational communication situations. This study highlights social disparities in remote workers' well-being, which arise from the complex interplay of SES either indirectly influencing the diversity in ICT usage or interacting with virtual organizational communication satisfaction and duration. This study advances remote work scholarship by restructuring theoretical discussions on social stratification and the digital divide reproduced within the evolving work environment.

Stalking, a widespread and distressing phenomenon, has recently garnered considerable attention. The advent of digital platforms has revolutionized the landscape of stalking, presenting new avenues and challenges for research. However, the impact of the coronavirus disease (COVID)-19 pandemic on stalking remains underexplored, despite extensive studies on similar crimes such as intimate partner violence and domestic violence. To address this gap, our study focused on Reddit, a prominent online platform with a diverse user base and open discussion. Through an analysis of posts from the subreddit (https://www.reddit.com/r/Stalking/), we sought to compare the discourse on stalking before and after the COVID-19 pandemic. We found notable shifts in stalking-related posts before and after the COVID-19 pandemic, particularly with the emergence of new topics centered on cyberstalking. We also observed that the experiences of stalking victims have significantly changed following the COVID-19 pandemic. Based on our findings, we discussed the implications for policies to help stalking victims.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots' and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.