Original Research ARTICLE
Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect
- 1Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States
- 2Department of Industrial engineering, Seoul National University, South Korea
This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, and compared human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. We created three stages of task demand that were performed sequentially (Low-High-Low) during AF-MATB tasks and identified differences in neural connectivity during workload transition. We used NASA-TLX, and ISA to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. We found significant hysteresis effects on performance and various brain network measures such as outflow of prefrontal cortex and connectivity magnitude. These findings would allow us to clarify the direction and strength of the Granger Causality under demand transitions. Therefore, these results involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. Furthermore the ability to compare data derived from human participants to data gathered by the ACT-R model allows researchers to better account for hysteresis effects in neuro-cognitive models in the future.
Keywords: ACT-R (Adaptive Control of Thought-Rational) cognitive model, EEG, neural correlates, Granger casuality, effective connectivity, multitasking, Cognitive Modeling
Received: 31 Aug 2018;
Accepted: 20 Dec 2018.
Edited by:Frederic Dehais, National Higher School of Aeronautics and Space, France
Reviewed by:Angela R. Harrivel, Langley Research Center, United States
Stephen Fairclough, Liverpool John Moores University, United Kingdom
Kurtulus Izzetoglu, Drexel University, United States
Copyright: © 2018 Kim, House, Yun and Nam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: PhD. Chang S. Nam, North Carolina State University, Edward P. Fitts Department of Industrial & Systems Engineering, Raleigh, United States, firstname.lastname@example.org