Our ability to multitask-focus on multiple tasks simultaneously-is one of the most critical functions of our cognitive system. This capability has shown to have relations to cognition and personality in empirical studies, which have received much attention recently. This review article integrates the available findings to examine how individual differences in multitasking behavior are linked with different cognitive constructs and personality traits to conceptualize what multitasking behavior represents. In this review, we highlight the methodological differences and theoretical conceptions. Cognitive constructs including executive functions (i.e., shifting, updating, and inhibition), working memory, relational integration, divided attention, reasoning, and prospective memory were investigated. Concerning personality, the traits of polychronicity, impulsivity, and the five-factor model were considered. A total of 43 studies met the inclusion criteria and entered the review. The research synthesis directs us to propose two new conceptual models to explain multitasking behavior as a psychometric construct. The first model demonstrates that individual differences in multitasking behavior can be explained by cognitive abilities. The second model proposes that personality traits constitute a moderating effect on the relation between multitasking behavior and cognition. Finally, we provide possible future directions for the line of research.
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http://dx.doi.org/10.1007/s00426-022-01700-z | DOI Listing |
Brain Behav
January 2025
Department of Aerospace Hygiene, Faculty of Aerospace Medicine, Air Force Medical University, Xi'an, China.
Introduction: Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters.
View Article and Find Full Text PDFInt J Behav Nutr Phys Act
January 2025
Department of Medicine, University of Otago, PO Box 56, Dunedin, 9010, New Zealand.
Background: Although evening screen time is thought to impair subsequent sleep, current measures are limited to questionnaires which seem unlikely to accurately assess screen time in youth. Given the ubiquitous nature of digital devices, improving measurement of screen time is required before related health effects can be appropriately determined. The aim of this study was to objectively quantify screen time before sleep using video camera footage.
View Article and Find Full Text PDFZhonghua Yu Fang Yi Xue Za Zhi
December 2024
MOE Key Laboratory of Population Health Across Life Cycle, Anhui Provincial Key Laboratory of Environment and Population Health across the Life Course, Hefei230032,China Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei230032,China.
To explore the direction of the association between smartphone multitasking behavior and comorbid symptoms of anxiety and depression (CAD) among college students. College students from one college located in Shanxi, Chongqing, and Shenzhen were selected between October and December 2021 using a multistage random cluster sampling method, and a follow-up visit was conducted in May 2022. The Assessment of Smartphone Multitasking for Adolescents, the Patient Health Questionnaire-9 Items, and the Generalized Anxiety Disorder Questionnaire-7 Items were used to assess the smartphone multitasking behaviors and CAD of college students.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Life Sciences, Centre for Clinical and Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
Multitasking (MT)-performing more than one task at a time-has become ubiquitous in everyday life. Understanding of how MT is learned could enable optimizing learning regimes for tasks and occupations that necessitate frequent MT. Previous research has distinguished between MT learning regimes in which all tasks are learned in parallel, single-task (ST) learning regimes in which all tasks are learned individually, and mixed learning regimes (Mix) in which MT and ST regimes are mixed.
View Article and Find Full Text PDFBMC Nurs
December 2024
Graduate School of Nursing, Osaka Metropolitan University, 3-7-30, Habikino, Habikino-shi, Osaka, 583-8555, Japan.
Background: Modeling is the learning of new patterns of behavior by observers through observation. In order for novice nurses to learn effectively in a busy, multi-tasking clinical environment, they need a learning strategy that is integrated into their daily work. Modeling is a necessary learning strategy for nurses because they learn skills, knowledge, and attitudes by observing senior nurses in action.
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