Exploring the effect of simulated Motion Conditions on Task Performance

This thesis explored the effect that four different areas of motion conditioning presented in a motion simulator had on defence-force based task performance. It is produced in conjunction with the Defence Science and Technology Organisation, Land Operations Division, to expand their understanding of how these conditions may affect their personnel. The four conditions are explored are Motion Sickness, Motion Fatigue, Motion Perception and Mental Workload under motion conditions. All studies involved first year psychology students enrolled at the University of Sydney in accordance with the University’s ethical guidelines (2013/388). In the study of Motion Sickness, nausea was shown to have very little detrimental effect on task performance. In long term driving exposure there was a slight negative effect on the reaction time that was linked to motion sickness symptoms. Two styles of driving were researched for Motion Fatigue: boredom and constant motion. The boredom drive was seen to have a slightly negative effect on performance compared to the motion drive. In analysing biomarkers of fatigue relevant to a defence context, the best indicators were respiratory rate and the Root Mean Square of Successive Differences between normal heart beats. In Motion Perception, 6-axes of motion at 3 intensities were tested using Defence force tasks to determine whether any one axis, or a certain intensity, negatively affected performance more than others. Higher errors occurred in the Roll direction. The Pitch direction was the least comfortable for participants. In the final chapter of Mental Workload under motion, increased workload did not have a great impact on performance, although further studies are needed. In an analysis of subjective scales of workload in simple tasks, participants were able to accurately determine their task performance. From a bio-measure perspective, pupil diameter and respiratory rate were found to be the most indicative of changing levels of workload