Kim Na Young, House Russell, Yun Myung H, Nam Chang S
Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States.
Department of Psychology, North Carolina State University, Raleigh, NC, United States.
Front Hum Neurosci. 2019 Jan 24;12:535. doi: 10.3389/fnhum.2018.00535. eCollection 2018.
This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. The results found significant hysteresis effects on performance and various brain network measures such as outflow of the prefrontal cortex and connectivity magnitude. These findings would assist in clarifying the direction and strength of the Granger Causality under demand transitions. As a result, these findings involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. 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.
本研究在多个分析层面调查了任务需求转换的影响，包括行为表现、主观评分和大脑有效连通性，同时将人类数据与思维适应性控制-理性（ACT-R）模拟数据进行比较。在自适应飞行多任务测试床（AF-MATB）任务期间，设计并依次执行了三个阶段的任务需求（低-高-低），并确定了工作负荷转换期间神经连通性的差异。使用美国国家航空航天局任务负荷指数（NASA-TLX）和即时自我评估（ISA）来测量任务需求转换中伴随滞后效应的主观心理工作负荷。结果发现，在表现以及各种大脑网络指标（如前额叶皮质流出和连通性大小）上存在显著的滞后效应。这些发现将有助于阐明需求转换下格兰杰因果关系的方向和强度。因此，这些涉及多任务环境中滞后效应神经机制的发现可能会用于神经工效学研究的应用中。将来自人类参与者的数据与ACT-R模型收集的数据进行比较的能力，使研究人员能够在未来更好地考虑神经认知模型中的滞后效应。
