The brain under cognitive workload: Neural networks underlying multitasking performance in the multi-attribute task battery

Lam, Timothy K.; Vartanian, Oshin; Hollands, Justin G.

doi:10.1016/j.neuropsychologia.2022.108350

Cited by 7 publications

(5 citation statements)

References 70 publications

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“…In the MATB, all tasks require constant processing, and more frequent task switching is required than in the SynWin—two key similarities to the TSWP that perhaps allow for a closer link between the paradigms. As a result, multitasking is generally rather costly than beneficial (Lam et al, 2022) in the MATB, another similarity to the TSWP. Ultimately, implementing a more realistic task environment other than the SynWin paradigm that can classify different processing modes could be promising for conducting a deeper investigation into the impact of individual processing modes in more complex situations.…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, simple tasks like letter or digit classification were used as a first strand to draw clear conclusions about cognitive limitations. In contrast, more applied research focuses on the overall performance consequences of realistic multitasking situations (Lam et al, 2022). Therefore, the second strand explores complex tasks that reflect real-world multitasking scenarios, incorporating additional task demands such as task complexity, environmental factors, and time pressure 1 , which are considered in the current study.…”

Section: Introductionmentioning

confidence: 99%

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Multitasking in Complex Environments: An Attempt to Generalize Individual Differences in Multitasking to a Realistic Task Setting

Schindler,

Bista,

Graffe

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Multitasking is a common feature of many modern work and home environments, and this study investigated the relationship between multitasking performance in two different paradigms: a more controlled task-switching paradigm (TSWP) and a more complex, semi-ecologically valid multitasking paradigm (SynWin). The study also explored whether parallel processors may have performance advantages in a complex dual-task environment. Results showed no significant correlation between individual multitasking efficiency in the TSWP and SynWin paradigms. Additionally, the results indicated that the combination of subtasks was the primary factor affecting performance in dual-task variants of the SynWin, rather than the use of a parallel processing mode. We conclude that there may be constraints with respect to the experimental conditions necessary to generalize findings from controlled multitasking paradigms to semi-ecologically valid tasks scenarios. Future research should prioritize efforts to understand how people multitask in more realistic settings and the underlying cognitive processes involved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multitasking in Complex Environments: An Attempt to Generalize Individual Differences in Multitasking to a Realistic Task Setting

Schindler,

Bista,

Graffe

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Recent studies using fMRI as a neuroimaging method supported this result. Lam et al ( 2022 ) found no significant difference when they analyzed the fMRI brain scans to verify the connectivity patterns of high and low MWL (hard and easy conditions) during the MATB task. Moreover, Causse et al ( 2022 ) analyzed the fMRI data during a novel NBack task.…”

Section: Discussionmentioning

confidence: 99%

“…The verbal NBack task is a visual‐verbal working memory task that mainly depends on inner attention, while the MATB task is a visual‐motor task that demands visual attention and motor execution (Chun et al, 2011 ; Ke et al, 2021 ; Kiyonaga & Egner, 2013 ). Meanwhile, the NBack task is usually related to specific activation of the frontal and parietal regions, while the MATB task involves more dynamic connections between networks (Lam et al, 2022 ; Owen et al, 2005 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies using fMRI as a neuroimaging method supported this result. Lam et al (2022) (Raichle, 2011;Raichle, 2015). Considering that the functional microstate model does not contain constraints on the spatial distribution of brain activity (Michel & Koenig, 2017), it is reasonable to find the difference of microstate intracortical sources during task conditions.…”

Section: Feature Selection and Classificationmentioning

confidence: 99%

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Evidence for modulation of EEG microstates by mental workload levels and task types

Chen,

Ke,

et al. 2023

Human Brain Mapping

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Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large‐scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi‐attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within‐task and 78% for cross‐task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

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Exploring the General Acceptability and User Experience of a Digital Therapeutic for Cognitive Training in a Singaporean Senior Population: A Qualitative Study (Preprint)

Kwek,

Leong,

Lee

et al. 2024

JMIR Formative Research

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The brain under cognitive workload: Neural networks underlying multitasking performance in the multi-attribute task battery

Cited by 7 publications

References 70 publications

Multitasking in Complex Environments: An Attempt to Generalize Individual Differences in Multitasking to a Realistic Task Setting

Multitasking in Complex Environments: An Attempt to Generalize Individual Differences in Multitasking to a Realistic Task Setting

Evidence for modulation of EEG microstates by mental workload levels and task types

Exploring the General Acceptability and User Experience of a Digital Therapeutic for Cognitive Training in a Singaporean Senior Population: A Qualitative Study (Preprint)

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