Performance decrements in multitasking have been explained by limitations in cognitive capacity, either modelled as static structural bottlenecks or as the scarcity of overall cognitive resources that prevent humans, or at least restrict them, from processing two tasks at the same time. However, recent research has shown that individual differences, flexible resource allocation, and prioritization of tasks cannot be fully explained by these accounts. We argue that understanding human multitasking as a choice and examining multitasking performance from the perspective of judgment and decision-making (JDM), may complement current dual-task theories. We outline two prominent theories from the area of JDM, namely Simple Heuristics and the Decision Field Theory, and adapt these theories to multitasking research. Here, we explain how computational modelling techniques and decision-making parameters used in JDM may provide a benefit to understanding multitasking costs and argue that these techniques and parameters have the potential to predict multitasking behavior in general, and also individual differences in behavior. Finally, we present the one-reason choice metaphor to explain a flexible use of limited capacity as well as changes in serial and parallel task processing. Based on this newly combined approach, we outline a concrete interdisciplinary future research program that we think will help to further develop multitasking research.
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http://dx.doi.org/10.1007/s00426-017-0938-7 | DOI Listing |
Sci Rep
January 2025
DeepClue Inc., Deajeon, Republic of Korea.
To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. Two raters graded the collateral perfusion scores of both conventional and DL-driven MRA collateral maps and measured the grading time.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR, China.
Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, JL431, China.
Multimodal sentiment analysis (MSA) aims to use a variety of sensors to obtain and process information to predict the intensity and polarity of human emotions. The main challenges faced by current multi-modal sentiment analysis include: how the model extracts emotional information in a single modality and realizes the complementary transmission of multimodal information; how to output relatively stable predictions even when the sentiment embodied in a single modality is inconsistent with the multi-modal label; how can the model ensure high accuracy when a single modal information is incomplete or the feature extraction performance not good. Traditional methods do not take into account the interaction of unimodal contextual information and multi-modal information.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Small molecule near-infrared (NIR) fluorophores play a critical role in disease diagnosis and early detection of various markers in living organisms. To accelerate their development and design, a deep learning platform, NIRFluor, was established to rapidly screen small molecule NIR fluorophores with the desired optical properties. The core component of NIRFluor is a state-of-the-art deep learning model trained on 5179 experimental big data.
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