Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
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Overview of Understanding and Quantifying Cognitive Load

인지부하 기전과 평가방법에 대한 고찰

  • Received : 2018.05.08
  • Accepted : 2018.05.25
  • Published : 2018.06.30
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Abstract

Objective: The aim of this study is to investigate physiological mechanisms underlying cognitive load and determine important factors that should be considered to quantify cognitive load. Background: Many studies have been conducted to propose measurement methods and effectively quantify effects that cognitive load has on user experiences, human performance, and human safety. However, few studies have been made to investigate which factors contributed to different findings of changes in physiological signals characterized with increasing cognitive load. Method: This study systematically reviews physiological mechanisms related to cognitive load based on working memory and selective attention theory. In order to determine the contributing factors to the different previous findings, subjective and objective measurement methods frequently and recently introduced in the literature have been overviewed. The contributing factors were determined by discussing the current advantages and limitations of the measurement methods. Results: Individual differences in inherent cognitive capability and differences in how to increase cognitive load affect human cognitive control, which results in the different findings of the physiological changes as a function of cognitive load. Minimizing the number of measures to quantify cognitive load is very important to address statistical issues such as increased false discovery rate. Conclusion: In order to evaluate accumulated cognitive load for ensuring human safety, objective measures indirectly reflecting neural connection between brain and heart can be considered while using multi-dimensional subjective measures such as NASA-TLX as an assistant method to validate the objective measures. In addition, EEG changes underlying cognitive load can be effectively measured by recording and analyzing brainwaves at just two midline electrodes (frontal and parietal) since the cortical regions are inextricably tied with cognitive control functions such as working memory and selective attention. Application: The findings in this overview are expected to provide practical guidelines to potential researchers who want to quantify cognitive load in their practical fields. With the updated knowledge of the measures, it can be possible to more precisely quantify cognitive load, determine contributing factors to the load, and then optimize various factors positively affecting man-machine interfaces.

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