4.4. EEG Power Spectrum Density
HMD, visual with auditory)
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The results revealed that performing the high-demand maintenance task using AR instruction led to an increase in the theta power in the parietal region as compared to the low-demand task, but no change was detected between the high and low-demand tasks when participants used paper-based instruction. In addition, there was a significant change between paper-based and AR instructions while participants performed the most demanding task, but no significant difference was found between the least demanding tasks. The increased theta power at the parietal region, when the participants performed the highly demanding task using AR instruction, may be an indicator of attentional orienting to a task-relevant class of event. On the other hand, participants who performed the high- and low-demanding tasks using paper-based instruction used the same class of cues for processing the information. Related to α power, the findings revealed that for the AR method, the α power during the high-demanding task demonstrated significantly larger values than during the low-demanding task across all regions of interest. In addition, for paper-based instruction, the α power of the temporal and occipital regions for the high-demanding task increased significantly relative to the low-demanding task but remained approximately the same over the frontal, central, and parietal regions. The results showed that at a low-demanding task, the α power in the central region for AR groups was found to be significantly increased relative to paper-based instruction but not significantly increased in the parietal, frontal, temporal, and occipital regions. These increases in alpha power are possibly due to sensory inhibition or internal processing demands. The drastic increase in alpha power attributed to the execution of the mental demand during the AR experience is remarkable. When compared to paper-based instruction, β power was found to be reduced in the central and occipital regions during AR instruction. In addition, the β power values were found to decrease as task complexity increased for all regions of interest, no matter what the instruction methods used. The increase of theta and alpha and the decrease of beta have probably resulted from the perceived intrinsic load that participants interpret as the intricacy of the objective affordances concerning their abilities to handle the maintenance task (i.e., their prior knowledge). As the task load increases, so does the amount of resources used, and more of the available capacity is used [The findings of this study are in line with previous studies, which reported that θ and α activities may be associated with memory, cognition, and attention [The overall findings of this study revealed that the use of AR instruction caused an increase in mental workload. Such an increase could be interpreted as a more germane cognitive load, which is desirable as it is associated with learning new skills and other information. So, AR fosters the construction of schemata (GCL) and mental models by activating the cognitive processes of the participants and encouraging them to invest more effort in learning. The germane load during AR is increased due to incorporating new information, as well as the development and updating of schema, which takes up some of the memory [Furthermore, the increased mental workload during AR usage may arise from the fact that delivering AR instructions is a novel method and participants have to learn new interaction techniques, which activates the cognitive processes of the participants to invest more effort into learning, raising mental effort. Hence, using AR as an instruction method, participants could better facilitate long-term knowledge and skill acquisition, manage their working memory load, and learn successfully. During the AR instruction method, the information is processed in working memory and stored in long-term memory, and knowledge, in accordance with schema theory, is stored and arranged in the mind in the shape of schemata. Also, during AR, the participants spent a greater amount of their mental resources on processes that were irrelevant to the actual task, like repressing instruction through animation, visual cues, and arrows. To overcome this, it is preferable to show an instruction visually when put together with animation. While presented instruction combined with animation requires visual resources, auditory instruction uses the phonological system of working memory [The current study has some limitations that are worth noting. The design was limited to a standing posture. The participants were restricted to standing during the task performance. Therefore, the effects of different postures (e.g., sitting and standing) should be investigated in the future. Second, upper body kinematics were not monitored in this study. Tracking inter-individual kinematic differences may aid in explaining observed differences in stiffness responses to complex tasks and instruction methods. In addition, all participants were male students. Future works will include female and other physiological signals (i.e., autonomic nervous system and upper arm muscle activity). Moreover, the target users were also novices, so it would be better to recruit experienced participants or experts to make the evaluations and comparisons more complete. Furthermore, the range of the participants’ ages was quite narrow, so it would be interesting to do more research on a broader age group in the future. The results and conclusions of this study were reached in light of the particular interfaces utilized for this user study (visual cues with animation), and the usage of different variations of the interface (visual with auditory), technology (HMD, HHD), and research designs (between, within, or mixed design) may yield different results and conclusions. Finally, this study also illustrated very effectively that the benefits promised by AR for maintenance applications will not be realized automatically.