These findings strongly support the use ofVR integrated training in the welding industry.

for their valuable guidance. I would like to offer my regards and blessings to Peihan Zhong and Kristopher Watts who supported and helped me during the completion of the project. Lastly, I dedicate this thesis to my parents and I am deeply indebted to them for their continuous support.

No abstract

The measurement of mental workload is a commonly used and widely accepted means of assessing cognitively driven human performance tasks. The aim of this study was to investigate the linkage between facial expression and mental workload in the performance of arithmetic tasks. Eighteen participants were recruited and asked to perform various levels of arithmetic tasks. Classical subjective and physiological measures were used to track mental workload levels; these measures included NASA Task Load Index (NASA TLX), Subjective Workload Assessment Technique (SWAT), and electroencephalogram (EEG). In addition we utilize and propose a new measurement based on facial expressions, coded with the Facial Action Coding System. The results showed that facial expression is a viable index for measuring mental workload.

Three aspects of visual inspection were considered in this study. The three aspects considered to effect inspection performance are (1) cognitive styles, (2) feedforward training (job aid), and (3) pacing. In this study, the Matching Familiar Figures Test (MFFT) was administered and the basic (control), static (self- pacing), and hybrid (systematic pacing) displays were used to investigate the pacing effect. The objective was to classify the inspectors into different categories via the MFFT based on their cognitive styles, and also to investigate inspection performance (accuracy and response time) affected by the job aids as cognitive styles. The results indicate that the MFFT is effective in all task conditions. The job aid was also found to have a positive impact on the overall performance.