Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

Jiali Huang; Zach Traylor; Sanghyun Choo; Chang S. Nam

doi:10.1177/1071181321651003

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

Jiali Huang, Zach Traylor, Sanghyun Choo, Chang S. Nam

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

Original language	English
Pages (from-to)	1337-1341
Number of pages	5
Journal	Proceedings of the Human Factors and Ergonomics Society
Volume	65
Issue number	1
DOIs	https://doi.org/10.1177/1071181321651003
State	Published - 2021
Externally published	Yes
Event	65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021 - Baltimore, United States Duration: Oct 3 2021 → Oct 8 2021

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title = "Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study",

abstract = "The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.",

author = "Jiali Huang and Zach Traylor and Sanghyun Choo and Nam, {Chang S.}",

note = "Publisher Copyright: {\textcopyright} 2021 by Human Factors and Ergonomics Society. All rights reserved.; 65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021 ; Conference date: 03-10-2021 Through 08-10-2021",

year = "2021",

doi = "10.1177/1071181321651003",

language = "English",

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TY - JOUR

T1 - Neural Correlates of Mental Workload During Multitasking

T2 - 65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021

AU - Huang, Jiali

AU - Traylor, Zach

AU - Choo, Sanghyun

AU - Nam, Chang S.

PY - 2021

Y1 - 2021

N2 - The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

AB - The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

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U2 - 10.1177/1071181321651003

DO - 10.1177/1071181321651003

M3 - Conference article

AN - SCOPUS:85171275787

SN - 1071-1813

VL - 65

SP - 1337

EP - 1341

JO - Proceedings of the Human Factors and Ergonomics Society

JF - Proceedings of the Human Factors and Ergonomics Society

IS - 1

Y2 - 3 October 2021 through 8 October 2021

ER -

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

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