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. Research using simple laboratory tasks has consistently shown that MT regimes are the most efficient-the so-called dual-task practice advantage. However, it is currently unclear which learning regimes are used in everyday life, and which regime people would prefer if given a choice. To answer these questions, 72 participants completed an online survey to describe their real-life experiences of MT learning (e.g., when learning to drive), their opinions about learning MT activities, and filled out the Multitasking Preference Inventory to assess polychronicity. Descriptive statistics showed that for everyday activities, particularly learning to drive, Mix regimes were both the most used and most preferred method, whereas MT regimes were the least preferred. A potential explanation is that everyday MT tasks are typically complex, and so people prefer to learn the individual tasks first, before combining the tasks into an MT learning regime. Preference to engage in MT, as assessed by the MPI, positively correlated (Pearson's r = .24) with preference for MT learning regimes, suggesting that individual differences in learning of complex everyday MT activities can be determined. In conclusion, everyday life multitasking activities such as learning to drive are mostly learned in Mix regimes, i.e. a combination of ST and MT training, and people's preference to learn such activities with MT regimes increases with their level of polychronicity.
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PLoS 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.
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December 2024
Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.
The rational design of molecules with the desired functionality presents a significant challenge in chemistry. Moreover, it is worth noting that making chemicals safe and sustainable is crucial to bringing them to the market. To address this, we propose a novel deep learning framework developed explicitly for inverse design of molecules with both functionality and biocompatibility.
View Article and Find Full Text PDFGenome Biol
December 2024
Biomedical Engineering, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system's capabilities for improving the accuracy of categorical prediction of cancer subtypes.
View Article and Find Full Text PDFExp Gerontol
December 2024
Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan. Electronic address:
Aluminum chloride (AlCl), a known neurotoxic and Alzheimerogenic metal disrupts redox homeostasis which plays a pivotal role in pathophysiology of neurodegenerative disorders, particularly cognitive decline. The current study was designed to unveil the long-term neuroprotective outcomes and efficacy of CoQ10 and curcumin low dose (100 mg/kg each) combination in 18-months old geriatric male Balb/c mice subjected to AlCl-prompted memory derangements (200 mg/kg in water bottles) for 28 days. The neuroprotective properties driven by antioxidant mechanisms were assessed via observing cellular pathology in key-memory related brain regions including the cornuammonis (CA3 and DG) and cortex 2/3 layer.
View Article and Find Full Text PDFPlant Physiol
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CNRS LGDP-UMR5096, 58 Av. Paul Alduy 66860 PERPIGNAN, FRANCE.
Acquired thermotolerance (also known as priming) is the ability of cells or organisms to survive acute heat stress if preceded by a milder one. In plants, acquired thermotolerance has been studied mainly at the transcriptional level, including recent descriptions of sophisticated regulatory circuits that are essential for this learning capacity. Here, we tested the involvement of polysome-related processes (translation and cotranslational mRNA decay (CTRD)) in Arabidopsis (Arabidopsis thaliana) thermotolerance using two heat stress regimes with and without a priming event.
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