Computers in Human Behavior最新文献

Online video social interaction is now commonplace following rapid technological advances and the Covid-19 pandemic. Whether computer mediated communication (CMC) fundamentally changes nonverbal behaviour and social responses from others is unknown. Here, we conducted a repeated measures experimental study (N = 66) comparing three types of dyadic social interactions: in person, online video call (with self-view) and online video call (no self-view). Facial videos were analysed using automated facial movement tracking (based on the Facial Action Coding System: FACS). Independent raters made first impression judgements across all conditions (N = 198). Overall, people were more facially expressive in person compared to CMC, but there were significant individual differences across participants. Agreeableness was associated with a particular increase in expressivity in person compared to online, while extroversion was associated with greater expressivity in online video calls, but only when self-view was visible. Older adults were most impacted by CMC and showed the greatest reduction in facial expressivity online compared to in person. The first impressions of observers did not differ as a function of CMC. These results suggest that CMC does alter facial expressivity during social interaction, but that there is an important interplay with individual differences.

Through creating an environment rich in computational and communication capabilities, ubiquitous computing gradually integrates it with human activities. Inspired by adaptive ubiquitous learning, various intelligent devices (e.g., roadside units and infrared sensors) deployed in the Internet of Vehicles (IoV) are expected to be critical to mitigating urban traffic congestion and enhancing travel safety. In addition, benefiting from the advantages of high mobility and real-time response, Unmanned Aerial Vehicles (UAVs) embody substantial prospects to assist IoV in efficiently and flexibly handling latency-sensitive, computation-intensive tasks. Nevertheless, due to time-varying demands and heterogeneous computing resources, it is challenging to provide effective service for mobile devices while guaranteeing high-quality data transmission. Therefore, a distributed service offloading system framework in UAV-enhanced IoV is designed. To minimize the service latency, a game theory-based distributed service offloading algorithm, named G-DSO, is proposed to realize adaptive ubiquitous learning for service request distribution. Finally, numerous experiments are implemented based on real-world service requirement datasets. Experimental results demonstrate that the proposed G-DSO approach improves the hit rate by 2.68% to 74.42% compared with four existing service offloading methods, verifying the effectiveness and good scalability of G-DSO.

The Internet of Vehicles (IoV) has the potential to bring about a revolutionary transformation in transportation through its influence on human behavior and interactions between users and vehicles. However, interoperability challenges between retailer organizations and manufacturers present a barrier to decision-making processes and impact the human-centric nature of the IoV. Ethical dilemmas arise as a result of the IoV’s inability to prevent accidents, particularly in critical situations. This study aims to enhance the IoV’s effectiveness by carefully selecting and improving essential attributes from various data sources, including sensors, GPS, 5G or 6G communication networks, and real-time data provisioning. To achieve the aim of the proposed study, a Multi-criterion Decision-making (MCDM) approach is proposed, which allows for the analysis and selection of optimal choices while taking into account various quantitative and qualitative factors. Despite the challenges posed by complex models and ambiguous data, MCDM remains an indispensable technique for aligning transportation systems with current expectations. The CRITIC and TOPSIS MCDM-enabled methodologies are employed to analyze IoV architecture, prioritizing significant elements that impact system performance and identifying optimal solutions by considering complications from worst-case scenarios. The study will assist engineers, scientists, and organizations to develop smart IoV systems that will cater to human needs by improving mobility and inspiration among users.

Automated vehicles (AVs) provide numerous advantages over manually operated vehicles, but the extent of these benefits depends on whether we engage with AVs safely and efficiently. To achieve such interactions with AVs, an appropriate – or calibrated – level of trust in AVs especially during critical scenarios, is fundamental. The trust level also impacts individuals' decisions regarding the utilisation of AV technology. This study investigates trust calibration and factors that influence how trust develops in AVs compared to human drivers. Two groups of participants underwent a driving simulation, experiencing either a ride in a human-driven taxi or an AV, during which a dangerous situation occurred. Before, during and after the simulation, the passengers’ trust was measured.

Pre-simulation trust was higher in the human driver than in the AV, but this difference disappeared after the simulation. Noticeably, during the simulation trust did not differ between the groups. Instead, the critical situation significantly influenced trust: following the dangerous incident, trust levels in both conditions dropped but recovered until the simulation ended. Additionally, self-esteem, which has been associated with trust in the past, was investigated. However, no significant relationship between self-esteem and trust was found in this study. Overall, the findings indicate that the dangerous situation prompted heightened caution among participants. A process of trust calibration was initiated in which the participants’ trust was highly susceptible to the driving style of the driver/AV. Moreover, the comparable evolution of trust in the human-driven vehicle and the AV, sheds light on the dynamics underlying attitudes towards AVs.

This study used video-based instruction to promote adolescents' online credibility evaluation skills and to examine whether storification of instructional videos can maintain students' situational interest and positive activating epistemic emotions during a four-lesson intervention better than non-storified instructional videos. The study also investigated whether storified instruction can enhance students' learning of online credibility evaluation skills better than non-storified instruction. The learning content of the videos was equivalent, differing only in terms of storification. Students participated in either a non-storified (n = 135) or storified (n = 115) video-based instruction (4 x 45-min lesson). In each of the first three lessons, students watched an instructional video explaining one credibility evaluation strategy. In the fourth lesson, the video introduced a case requiring students to apply the taught strategies. After watching the video, students' situational interest and positive activating epistemic emotions (i.e., curiosity and excitement) were measured. Then, students practiced the taught strategies with learning tasks. Students' credibility evaluation skills were measured before and after the intervention with an online credibility evaluation task. The latent growth curve model showed that non-storified and storified videos similarly maintained students' situational interest. No decline was observed. However, a small decline in positive activating epistemic emotions was observed in both instructional conditions. Students' credibility evaluation skills improved equally across instructions. The results underline that professionally produced videos following several multimedia design principles can trigger and maintain situational interest and enhance students’ online credibility evaluation skills without the need to incorporate additional storified elements.

This study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did not restrict the diversity of students’ perspectives. These findings highlight the nuanced implications of digital tools on learning, suggesting that while LLMs can decrease the cognitive burden associated with information gathering during a learning task, they may not promote deeper engagement with content necessary for high-quality learning per se.

The effects of abstinence from digital services and/or devices (aka “digital detox”) on mental health have received increased interest in recent years. Regular breaks and abstinent periods are advised to help individuals who overuse digital technology, but the psychological benefits and related consequences require empirical attention. While some studies have examined the impact of abstinence from smartphone or social networks (SN) on mental health, there is a dearth of literature on the effects of abstinence from Instant Messaging (IM) services. This study examined the association between a 72-h abstinence from IM and subjective craving toward IM, IM withdrawal-like symptoms, affect (positive and negative), and time spent on IM. Thirty-five undergraduates who regularly engaged in IM were assessed one day before (baseline), during (daily), one day after, and one week after refraining from using IM for 72 h. Results revealed a moderate-to-large decrease in craving (η2 = 0.438) and negative affect (η2 = 0.386) during and after the abstinence, whereas withdrawal-like symptoms and positive affect remained stable (η2 between 0.009 and 0.140). One week after abstinence, participants reported a small-to-moderate decrease in their IM use (d = 0.32). These results suggest that abstinence from IM is associated with reduced preoccupation and/or obsession (i.e., craving) with this use and lower negative affect. However, withdrawal-like symptoms and positive affect did not change. These findings support recommendations that IM users experiencing negative affect states may benefit from short-term abstinence. Further research should investigate strategies to support users in undertaking abstinence and regulating their digital device use.

Toxic behavior has been impacting players in online multiplayer environments since their inception. Griefing is a type of toxic behavior that focuses on player-to-player in-game disruption and is quite prevalent. However, research into the extent of the impact is still scarce. The present study investigated the impact on the psychological needs of autonomy, competence, and relatedness, as defined by the self-determination theory, for players that perform griefing (the griefers) and those subjected to griefing (the griefed). A sample of 656 respondents from massively multiplayer online role-playing game communities participated in the study. The results discovered that for the majority of players there is no change to their wellbeing, but that when there was a change, the griefed players in general were impacted more negatively, and the perpetrators were impacted more positively. Significant associations also revealed that the magnitude of impacts increased as the player was subjected to or performed griefing more frequently.

This study investigated the reciprocal loop between catch-up sleep and mobile phone dependency from late childhood to late adolescence, distinguishing between inter-individual and intra-individual variations. Data from two nationally representative South Korean birth cohorts (N = 4,729, 48.8% female) were collected annually, assessing participants seven times from Grade 4 to Grade 10 or Grade 7 to post-high school. Findings from a cross-lagged panel model (CLPM) and a random-intercept CLPM showed that students who were more dependent on their mobile phones, either compared to others or to their own averages, were likely to report greater catch-up sleep from Grade 4 through Grade 7 in the younger cohort and from Grade 7 through Grade 10 in the older cohort. Likewise, students with greater catch-up sleep, compared to others or to their own averages, tended to rely more on their mobile phones from Grade 4 to Grade 5 in the younger cohort and from Grade 7 to Grade 8 in the older cohort. The CLPM findings further revealed that in the younger cohort, students with higher mobile phone dependency than their peers were more likely to increase catch-up sleep from Grade 7 to Grade 8, and in the older cohort, from Grade 10 to Grade 11. Similarly, higher catch-up sleep led to increased mobile phone dependency from Grade 9 to Grade 10 in the younger cohort, and from Grade 12 to the year post-high school in the older cohort. These patterns suggest consistent tendencies at both inter- and intra-adolescent levels.