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## things that affect human performance

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Human performance can be affected by practice during performing different levels of mazes.

To examine how the practice was influenced the human performance in sensorimotor tasks and understand some of human factors, 5 different levels of mazes that started with a very simple level of mazes and end up with very complex maze was conducted. Each student was tested with 5 levels of difficulty x 15 maze mazes (75 mazes), then the time taken across the 15 trials for each level of difficulty and the distance covered was calculated. The graph showing the main performance of students for 5 different levels of mazes and the time that students took to complete the task. Results indicated that the time was increased systematically as the difficulty level of mazes increased, and there was a treatment effect as a function of maze difficulty between columns F (1.939,42.66) = 71.84 and p <0.0001, and individual between rows effect F (22,88) = 4.729 and p <0.0001 as shown in (Fig.1).

Figure 1: Maze difficulty levels vs Time of completion. The graph showing the main performance of students for 5 different levels of mazes and the time that students took to complete the task. Each student was tested with 5 levels of difficulty x 15 maze mazes (75 mazes), then the time taken across the 15 trials for each level of difficulty and the distance covered was calculated. The time was increased systematically as the difficulty level of mazes increased. There was a treatment effect as a function of maze difficulty between columns F (1.939,42.66) = 71.84 and p <0.0001, and individual between rows effect F (22,88) = 4.729 and p <0.0001. Values are expressed as means (± SEM). Groups were compared with one-way RM ANOVA to calculate the level of difficulty on time taken in each task. An analysis was run with Graph Pad Prism version 8. Human performance can be improved by practice due to the learning effect. To examine how the practice was influenced the human performance in sensorimotor tasks in maze level 5 which is a very complex maze, 9 trials were conducted. Each student was tested with maze level 5, then the time taken across the 9 trials and the distance covered was calculated. Results indicated that there was a systematic declined across the 9 trials and there was no significant difference in the 9 trials p > 0.7 as shown in (Fig.2). This suggested that the experience of practice provided students with some improvements and changed the performance.

Figure 2: Trial number vs Time of completion. The graph showing the main performance of students in maze level 5 and the time that students took to complete the task. Each student was tested with maze level 5, then the time taken across the 9 trials and the distance covered was calculated. There is a systematic declined across the 9 trials. Values are expressed as means (± SEM). For analysing data, linear regression was used to calculate the level of difficulty on time taken in maze 5. There was no significant difference in the 9 trials p > 0.7 and the coefficient of determination (R2) value of 0.2 which suggests a reasonable fit of the trend line across most of the data points. An analysis was run with Graph Pad Prism version 8.

Distortion and feedback effects on human performance.

To examine how the practice can influence the human performance in sensorimotor tasks in maze level 3 which is a moderate difficulty maze, 10 trials were conducted. Each student was tested with maze level 3 or 4 while wearing a pair of prism glasses, then the time taken across the 10 trials and the distance covered was calculated. Two forms of feedbacks were provided during the 10 trials, covert during moving the mouse and overt after completed the task after 5 min. The results indicated that subject performance was significantly improved during the condition of distortion + feedback compared to the normal conditions p = 0.0376. On the other hand, there was no significant difference in the performance under distorted condition compared to the other two conditions as depicted in (Fig.3). There was a treatment effect (different between columns F (1.064,10.64) = 5.496 and p=0.0380) and individual effect (different between columns F (10,20) = 2.222 and p=0.0617).

Figure 3: Distortion and feedback effects on human performance. Each student was tested with maze level 3 or 4 while wearing a pair of prism glasses, then the time taken across the 10 trials and the distance covered was calculated. Two forms of feedbacks were provided during the 10 trials, covert during moving the mouse and overt after completed the task after 5 min. The graph showing that values shift up quietly significantly in the case of distortion and one student did hugely worse under this condition, and the rest of the students did also worse compared to the normal condition. However, the subject performance was significantly improved during the condition of distortion + feedback compared to the normal conditions. There was a treatment effect (different between columns F (1.064,10.64) = 5.496 and p=0.0380) and individual effect (different between columns F (10,20) = 2.222 and p=0.0617). Values are expressed as means (± SEM). Groups were compared with one-way RM ANOVA to calculate the level of difficulty on time taken in each task. An analysis was run with Graph Pad Prism version 8. * p = 0.0376 which point out significant differences between normal vs distortion + feedback.

The effect of increasing demands on attentional systems on human performance.

To investigate how cognitive load affect human performance, a new set of 10 mazes was conducted at level 3 or 4. During the experiment, each student was asked to count a random number between 80-90 backwards in 3 seconds for 5 trials, and for the remaining 5 trials, they asked to do the multiplication table of the 5 random numbers (7, 9, 13, 6, and 12 times tables). The time taken across the 10 trials for level 3 or 4 of difficulty and the distance covered was calculated. Under the normal condition, there is no improvement in the trials compared to the condition with a cognitive load. The student’s performance was better in the condition with a cognitive load as depicted in (Fig.4). There is a significant effect of the subject F (10, 99) = 9.063 and P<0.0001, and task condition F (1, 99) = 21.74 and P<0.0001, but there is no significant effect of trial number F (99, 99) = 0.9119 and P =0.6764.

Figure 4: The effect of increasing demands on attentional systems on human performance. The graph showing the main performance of students for 3 or 4 different levels of mazes and the time that students took to complete the task. During the experiment, a new set of 10 mazes was conducted, and each student was asked to perform certain mathematical calculations. Under the rest condition, there is no improvement in the trials compared to the condition with a cognitive load. The student’s performance was better in the condition with a cognitive load. There is a significant effect of the subject F (10, 99) = 9.063 and P<0.0001, and of the task condition F (1, 99) = 21.74 and P<0.0001, but there is no significant effect of trial number F (99, 99) = 0.9119 and P =0.6764. Groups were compared with two-way RM ANOVA to calculate the level of difficulty on time taken in each task. An analysis was run with Graph Pad Prism version 8.

The effect of circadian variations in alertness and arousal on human performance.

To investigate how exercise effects on human performance, each student was tested with 3 or 4 levels of difficulty after they did 30 seconds of cycling exercise. Students were first asked to calibrate their level of perceived exertion, sit on the bicycle ergometers and tried cycling for a short time at levels 13, 15, 17 and 20. After 2 min without doing any cycling, they were asked to cycle for 30 seconds a rating level of 15, then they were asked to do 10 new mazes at levels of difficulty. Then the time taken across the 15 trials for each level of difficulty and the distance covered was calculated. The results indicated that exercise levels have no impact on performance as shown in (Fig.5). There was a significant difference between subject F (11, 108) = 4.881 and P<0.0001, and there was a difference between a trial number F (108, 216) = 1.565 and P = 0.0029, but exercise levels had no impact on performance F (1.973, 213.1) = 1.524 and P=0.2203.

Figure 5: The effect of circadian variations in alertness and arousal on human performance. The graph shows how different exercises can affect the human performance. Each student was tested with 3 or 4 levels of difficulty after they did 30 seconds of cycling exercise at levels 13, 15, 17 and 20, and after 2 min without doing any cycling at the level of 15. From the graph, we can see that exercise levels have no impact on performance. There was a significant difference between subject F (11, 108) = 4.881 and P<0.0001, and there was a difference between a trial number F (108, 216) = 1.565 and P = 0.0029, but exercise levels had no impact on performance F (1.973, 213.1) = 1.524 and P=0.2203. Groups were compared with two-way RM ANOVA to calculate the level of difficulty on time taken in each task. An analysis was run with Graph Pad Prism version 8.

Phase effects on morning trials.

Figure 6: Phase effects on morning trials. To look if the people who were in phase with the circadian rhythms performed better than people who are out of phase, 5 different levels of mazes were conducted. Students were asked to do the questionnaire to classify them as a morning or afternoon people. Half of the morning people were asked to do the first sit of the 5 mazes levels in the morning and another half student to do in the afternoon, and for the afternoon did the same thing. In the morning 2 groups of students do the mazes, one group were the morning people (there are in the phase of the circadian rhythms) and the other group were the afternoon people (out of phase). Figure 6 illustrated that the maze level was significant, as the maze becomes harder, the student took longer time to perform the task F (4, 32) = 26.12 and p< 0.0001, the circadian phase (In vs out) had no impact F (1, 8) = 1.018 and p = 0.3425. There was a significant difference between subject F (8, 32) = 3.685 and P = 0.0038. The afternoon people did worse in the morning compared to the morning people. This suggested that the classification may not strong enough, there was not enough of the separation between in the morning and afternoon people. Moreover, the time the students started performing the task may affect their performance because the majority of the students did the task between 11 to 12.

Figure 6: Phase effects on morning trials. 5 different levels of mazes were conducted. Students were asked to do the questionnaire to classify them as a morning or afternoon people. In the morning 2 groups of students do the mazes, one group were the morning people (there are in the phase of the circadian rhythms) and the other group were the afternoon people (out of phase). This graph shows that the maze level was significant, as the maze become harder, student took longer time to perform the task F (4, 32) = 26.12 and p< 0.0001, the circadian phase (In vs out) had no impact F (1, 8) = 1.018 and p = 0.3425. There was a significant difference between subject F (8, 32) = 3.685 and P = 0.0038. The afternoon people did worse in the morning compared to morning people. Groups were compared with two-way RM ANOVA to calculate the level of difficulty on time taken in each task. An analysis was run with Graph Pad Prism version 8.

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