Computers in Human Behavior最新文献

We examined the effects of media multi- vs. single-tasking on performance in a scenario that resembled the media multitasking requirement of many contemporary office workplaces.

Older (n = 117, M_age = 70.99 years, SD = 5.18) and younger adults (n = 109, M_age = 22.86, SD = 3.93) performed a repetitive computer-based reading task (Task 1) that, in the multitasking condition, was repeatedly interrupted by news app messages that the participants had to read for comprehension (Task 2). In the single-tasking condition, the messages appeared after Task 1 was completed. In both conditions, participants afterwards answered questions about the message content. Performance in Task 1 was poorer in the multi- than in the single-tasking condition, but there was no evidence of greater multitasking costs in the older compared to the younger adults. Regarding Task 2, there was no indication of multitasking costs in any age group, but older adults showed poorer comprehension, regardless the task requirements. This effect of age was mediated via working memory updating abilities. Thus, the way information is commonly presented in social media, that is, usually bit-by-bit and not ordered by topic, seems to disadvantage older readers due to poorer updating abilities.

All in all, the results provide some indication that, in terms of task accuracy, older adults do not appear to be at a disadvantage compared to younger adults when it comes to coping with the multitasking demands of digitalized societies.

Non-fungible tokens (NFTs) represent a multibillion-dollar global market. While considerable speculation exists about the future utility of NFTs, there has been limited research into the consumer behaviors of market participants. This research paper examines the motivations of NFT buyers through the lens of self-determination theory. Using a sample of 482 participants, the authors tested a conceptual framework to better understand both NFT buyers' intrinsic and extrinsic motivations. This study expands the literature on NFTs in three different ways: i) it is the first study, as far as we know, to focus exclusively on NFT buyers and their motivations in purchasing NFTs; ii) it explores a variety of potential motivations theorized in the literature; and iii) it tests the expected future value of NFTs as both a motivation and as a moderator for NFT buyers. The authors determined that intrinsic motivation had the most substantial effect on purchase intention, and the expectations of NFTs’ future value positively moderated the effect of amotivation on purchase intention. In contrast, high expectations of future value moderated the effect of external regulation on purchase intention. The results suggest that NFT buyers are not as impacted by potential social or monetary gain as often characterized in the academic literature but behave more like traditional buyers of luxury goods.

Social media cues can distract people from working on intended tasks. Accordingly, in the context of social media, this study examines what influences a person's distractibility, specifically whether social media cues are more distracting than neutral cues. Across three experiments (N = 246), we measured user distractibility with the filter task paradigm, a measure of the ability to filter out distractions. The results showed that social media cues are not more visually distracting than complex neutral cues, but they are more distracting than simple neutral cues. In addition, distractibility increases with an increase in the number of distractors. Moreover, the findings show that individual differences (i.e. self-control, impulsivity, FoMO, problematic social media use) did not impact distractibility (Experiment 1). Experiment 2 found that state cyberostracism influenced distractibility, whereby socially excluded participants were more distracted than those who felt included. State fear of missing out (FoMO) did not influence distractibility (Experiment 3). This study contributes to the understanding of the underlying mechanisms of social media distraction by finding that social media cues are not more distracting than other neutral, more complex cues. This might be due to the high task demand under which we measured distractibility in our experiments. Future work is needed to find out more about differences in social media distraction and influencing factors of user's distractibility.

In the information age, many individuals turn to digital influencers' videos on social media as a source of stress relief, emotional support, and a sense of friendship-like connection, and this process of emotional transmission is well described by social contagion theory. Given the limited research on the role of follower stickiness in determining the market value of digital influencers, we integrate the characteristics of digital influencers, incorporating social compensation theory and empathy as follower traits, and demonstrate a positive impact on parasocial interaction. An online survey was conducted with 597 participants who had watched digital influencer videos. This study highlights the role of social contagion theory, offering a novel perspective that highlights the importance of improving followers' online social well-being and stickiness after interacting with digital influencers through emotional contagion. Furthermore, our findings highlight the mediating role of online social well-being in strengthening stickiness between followers and digital influencers.

Habitual behaviors significantly shape our daily actions. Furthermore, habit formation is proposed as a key mechanism contributing to the development and maintenance of addiction. However, the neural substrates underlying daily habitual tendencies and their contribution to behavioral addiction symptoms in everyday life remain poorly understood. To explore these questions, we conducted a comprehensive analysis of data from 219 individuals who underwent neuroimaging (structural MRI) assessments alongside evaluations of their daily habitual tendencies and symptoms of Internet Gaming Disorder (IGD) and Problematic Smartphone Use (PSU). Using voxel-based morphometry, meta-analytic decoding, and mediation analysis, we found that daily habitual tendencies were positively correlated with larger gray matter volumes in the ventromedial prefrontal cortex (vmPFC), precuneus, superior frontal gyrus (SFG), inferior temporal gyrus (ITG), and supplementary motor area (SMA). Notably, the midline regions, including the vmPFC and precuneus, play a crucial role in value-based computation, emotional regulation, social cognition, and self-referential thinking. Individual variations in gray matter volumes within these regions served as mediators, influencing the bidirectional relationship between daily habitual tendencies and IGD symptoms. However, vmPFC variations were specifically found to mediate the pathway from PSU to daily habitual tendencies. Our findings suggest that the morphological architecture of the vmPFC and precuneus is associated with habitual tendencies in daily life and may mediate the development of addictive behaviors. This study contributes to a more nuanced understanding of the neuroanatomical basis of daily habitual tendencies and their role in addictive behaviors.

Virtual economies with 3D assets have been studied for decades, often in the context of entertainment, but the concept of the metaverse as a workplace platform has only recently begun to take hold. This research addresses a gap in our understanding of how the enhanced ability for impression management in the metaverse workplace may relate to worker well-being and equity. We explore how demographics and previous virtual meeting (VM) experiences relate to people's valuation of self-presentation in the metaverse, reflected by willingness to pay for avatar customizations in a work context. Survey responses from a general population of adults (n = 553) suggest that the valuation of avatar customization in the metaverse workplace was predicted by VM fatigue, gamers' propensity to purchase virtual items, and demographics. People of color and women exhibited higher intentions to purchase avatar customization in the metaverse workplace. These results support the reasoning that the demand for impression management in the metaverse workplace will likely motivate avatar customization, and that gamers are likely to be the early adopters of paid avatar customization options in the non-game metaverse. This study contributes to an understanding of the role avatar customization has for a self-presentation strategy to meet norms in the future metaverse workplace. We discuss implications of VM fatigue and self-presentation concerns from the perspective of an equitable avatar-mediated workplace.

Lung diseases are one of the prime reasons for mortality globally, having an estimated five million per year fatal cases worldwide. This is a growing global concern so early detection using a Computed Tomography (CT) scan is crucial to prevent loss that grabs the attention of cutting-edge technologies to bring the concept called “Smart Healthcare”. However, the paucity and heterogeneity of medical data across the globe make it challenging to develop a global classification framework, while the other concerns that arise from legal and privacy leakage become an obstacle for data sharing as single source data is hardly enough to represent universal. Federated Learning has issued a solution to licensing research and data heterogeneity concerns allowing collaborative and on-device learning without sharing raw data. FL faces security issues such as Denial-of-service, Reverse engineering attacks, etc, where it is impossible to track the data and store it securely. The study proposes an innovative framework that combines Blockchain technology and Federated Learning (FL) to enable collaborative model training while preserving data privacy. Through this approach, patient data is authenticated using blockchain, and FL facilitates on-device learning without sharing raw data. The framework utilizes the DenseNet-201 model for lung disease classification, with model parameter aggregation using the FedAvg algorithm and storage on the blockchain via IPFS. Finally, we have conducted a substantial investigation with Python and its widely used libraries, like TensorFlow and Scikit-Learn to demonstrate that the algorithm accurately detects lung diseases and attained an accuracy, precision, recall, and F1-score of 90%.

Background

Cyberbullying victimization increases psychopathological symptoms among adolescents. Brain-derived neurotrophic factor (BDNF) gene rs6265 polymorphism or vagal tone as individual diathesis or plasticity has been identified to interact with adverse experiences to affect developmental psychopathology. However, how these different individual factors combined to interact with adverse experiences to affect developmental psychopathology remains unknown. This study sought to investigate the integrally moderating role of BDNF gene rs6265 polymorphism and vagal tone in the relationship between cyberbullying victimization and psychopathological symptoms among adolescents.

Methods

The Cyberbullying Victimization Scale (CVS) and the Youth Self-Report (YSR-2001-CV) were administered to 382 junior school students, their saliva were collected for DNA genotyping and 5 min resting high frequency heart rate variability (HF-HRV) were recorded for assessing vagal tone.

Results

A positive relation between cyberbullying victimization and psychopathological symptoms was found, and this relation was independently and jointly moderated by resting HF-HRV and BDNF gene rs6265 polymorphism. Specifically, the relation was weaker only among adolescents with Val/Val genotype and low resting HF-HRV, but not those with Val/Val genotype and high resting HF-HRV, or Met/Met or Val/Met genotype regardless of resting HF-HRV.

Conclusion

These findings suggest a combined moderating effect of BDNF gene rs6265 polymorphism and vagal tone on the relation between cyberbullying victimization and psychopathological symptoms among adolescents.

This article provides a comprehensive review of current practices and methodologies within the field of learning analytics, structured around a dedicated closed-loop framework. This framework effectively integrates various aspects of learning analytics into a cohesive framework, emphasizing the interplay between data collection, processing and analysis, as well as adaptivity and personalization, all connected by the learners involved and underpinned by educational and psychological theory. In reviewing each step of the closed loop, the article delves into the advancements in data collection, exploring how technological progress has expanded data collection methods, particularly focusing on the potential of multimodal data acquisition and how theory can inform this step. The processing and analysis step is thoroughly reviewed, highlighting a range of methods including machine learning and AI, and discussing the critical balance between prediction accuracy and interpretability. The adaptivity and personalization step examines the current state of research, underscoring significant gaps and the necessity for theory-informed, personalized learning interventions. Overall, the article underscores the importance of interdisciplinarity in learning analytics, advocating for the integration of insights from various fields to address challenges such as ethical data usage and the creation of quality learning experiences. This framework and review aim to guide future research and practice in learning analytics, promoting the development of effective, learner-centric educational environments driven by balancing data-driven insights and theoretical understanding.