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Media multitasking entails simultaneously engaging in multiple tasks when at least one of the tasks involves media (e.g., online activities and streaming videos). Across two studies, we investigated one potential trigger of media multitasking, state boredom, and its relation to media multitasking. To this end, we manipulated participants' levels of state boredom using video mood inductions prior to administering an attention-demanding 2-back task during which participants could media multitask by playing a task-irrelevant video. We also examined whether trait boredom proneness was associated media multitasking. We found no direct evidence that state boredom leads to media multitasking. However, trait boredom proneness correlated with greater amounts of media multitasking in Experiment 1, but not in Experiment 2. Surprisingly, in both experiments, post-task ratings of state boredom were equivalent across conditions, alerting us to the short-lived effects of video mood inductions and the boring nature of cognitive tasks.
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http://dx.doi.org/10.3389/fpsyg.2022.807667 | DOI Listing |
Sci Rep
August 2025
School of Intelligent Manufacturing Engineering, Shanxi University of Electronic Science and Technology, Linfen, 041000, Shanxi Province, China.
Early rumor detection on social media requires joint modeling of semantic content and dynamic propagation patterns, a critical yet challenging task in text mining. While existing methods often focus exclusively on either contextual information or user behavior, we propose MLI-GRA, a heterogeneous graph reconstruction approach that integrates both through multi-level interactive fusion. We first employ a graph auto-encoder framework to integrate semantic information and propagation patterns with the multiple graph convolutional network (GCN) and the graph reconstruction module.
View Article and Find Full Text PDFZhonghua Yi Xue Za Zhi
August 2025
Department of Neurology, Peking Union Medical College Hospital, Beijing 100730, China.
To establish and validate an automated detection model for interictal epileptiform discharges (IED) through a multi-task learning algorithm that integrates sleep features, providing more precise electroencephalogram (EEG) interpretation support for clinical practice. Based on convolutional neural networks, a multi-task learning model Siamese-ES that integrates sleep feature was developed. The dataset comprised EEG recordings from 150 patients at Peking Union Medical College Hospital Epilepsy Center from March 2019 to April 2023, of which 140 cases were diagnosed with epilepsy, and the other 10 cases were non-epileptic patients without IED.
View Article and Find Full Text PDFPediatr Res
August 2025
Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
Cureus
July 2025
Department of Neuroscience, Satani Research Centre, Ahmedabad, IND.
Background and objective Extensive use of social media raises concerns regarding its psychological and neurophysiological impact. Although behavioral effects have been the focus of earlier research, there are scarce empirical data addressing the degree to which real-time brain activity alters with social media use. This research aimed to examine the neurocognitive impact of social media usage by assessing brainwave activity via electroencephalography (EEG) to determine specific patterns of neural engagement as well as cognitive/emotional responses.
View Article and Find Full Text PDFIntroduction: The ubiquity of smartphone devices in our everyday lives has been widely recognized as a potential challenge to the quality of parent-child interactions. The aim of this study was to experimentally examine the effects of mothers' smartphone use on their children's affect regulation and on the quality of mother-child interactions, indicated by emotional availability of the dyad and maternal responsiveness. Additionally, we investigated the associations between mothers' behaviors to maintain contact with their children during smartphone use and their children's affect regulation.
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