ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2013, Vol. 21 ›› Issue (8): 1390-1399.doi: 10.3724/SP.J.1042.2013.01390

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Mental Workload Assessment: From the Perspective of Neuroergonomics

JIA Huibin;ZHAO Qingbai;ZHOU Zhijin   

  1. (1 Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China) (2 Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China)
  • Received:2012-12-19 Online:2013-08-15 Published:2013-08-15
  • Contact: ZHAO Qingbai;ZHOU Zhijin

Abstract: The assessment of mental workload is undergoing transition from traditional ergonomics to neuroergonomics. The neuroimaging techniques such as electroencephalography (EEG), event-related potentials (ERPs), functional magnetic resonance imaging (fMRI), functional Near-infrared spectroscopy (fNIRS) and transcranial doppler (TCD) provide the strong supports for this revolution. It is found that under single-task condition, with the increase of mental workload level, alpha-band power decreases, theta-band power increases. In addition, the cerebral blood flow in the regions of the prefrontal cortex (PFC), the average oxygenation changes in the regions of left inferior frontal gyrus (LIFG) and the cerebral blood flow velocity (CBFV) also increase. Under dual-task condition, with the increase of mental workload level, the amplitudes of several ERPs components elicited by the secondary task stimuli (e.g., N1, Novelty P3, P3b, etc) decrease. Based on these findings, researchers have achieved the real-time online evaluation of mental workload by pattern classification algorithms (e.g.,artificial neural network, support vector machine). But all of these techniques have their own advantages and disadvantages on several aspects (i.e., sensitivity, diagnosticity, primary task intrusion, implementation requirements, acceptability and reliability). In the future, researchers should promote the combination of these neuroimaging technologies, improve their acceptability and enhance their sensitivity and diagnosticity via pattern recognition algorithms.

Key words: mental workload, neuroergonomics, brain electrical activity, hemodynamics, optical imaging