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In two experiments, we sought to determine whether (a) people are aware of the frequently observed performance costs associated with engaging in media multitasking (Experiment 1), and (b) if so, whether they modulate the extent to which they engage in multitasking as a function of task demand (Experiment 2). In Experiment 1, participants completed a high-demand task (2-back) both independently and while a video was simultaneously presented. To determine whether people were sensitive to the impact that the concurrent video had on primary-task performance, subjective estimates of performance were collected following both trial types (No-Video vs. Video trials), as were explicit beliefs about the influence of the video on performance. In Experiment 2, we modified our paradigm by allowing participants to turn the video on and off at their discretion, and had them complete either a high-demand task (2-back) or a low-demand task (0-back). Findings from Experiment 1 indicated that people are sensitive to the magnitude of the decrement that media multitasking has on primary-task performance. In addition, findings from Experiment 2 indicated that people modulate the extent to which they engage in media multitasking in accordance with the demands of their primary task. In particular, participants completing the high-demand task were more likely to turn off the optional video stream compared to those completing the low-demand task. The results suggest that people media multitask in a strategic manner by balancing considerations of task performance with other potential concerns.
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http://dx.doi.org/10.1007/s00426-018-1056-x | 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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