## 3 Model Validation
### Results and Discussion  
Figure 6 and Table 3 demonstrate that most of the subjects and the model performed task switching just after the completion of the ongoing task, when the alternative task still remained. Additionally, the root mean square error (RMSE) value between the subjects and model data was 6.791. Separate analyses of variance (ANOVAs) were performed to examine the trends in the results of task switching percentage just after a subtask in each of the subjects and the model data, and the statistical results included \(p\)-values of 0.058 and 0.062, respectively. The results demonstrate that most of the task switching in both the subjects and model occurred just after the completion of the ongoing subtask, regardless of the level of difficulty in the ongoing task, which confirms Hypothesis 1. However, Figure 6 demonstrates that the subject data are somewhat lower and more volatile than the model data, which could be interpreted to indicate that humans are more conservative in task switching than the model, and individual differences in human behavior have a somewhat significant effect on task switching. In the context of Hypothesis 2, Figure 7 and Table 4 present the results of the subject and model data. Separate nonparametric ANOVAs were performed to examine the trends in the results of the average number of task switching occurrences during the execution of the ongoing subtask of each subject and the model. In addition, the statistical results include \(p\)-values of 0.049 (subjects) and 0.005 (model), which implies that the number of task switching occurrences during the execution of a subtask in the ongoing task significantly increased for both the subjects and the model, in accordance with the increased difficulty level of the ongoing task. However, Figure 5 also illustrates that the subject data were somewhat lower than the model data (RMSE = 1.09). The results could also be interpreted to indicate that humans are more conservative in task switching than the model. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Level of Difficulty**} & \multicolumn{2}{c}{**Mean (Standard Deviation)**} \\ \cline{2-3}  & **Subjects** & **Model** \\ \hline Level 1 & 95.29 (5.91) & 95.74 (6.78) \\ Level 2 & 91.30 (12.40) & 99.17 (2.89) \\ Level 3 & 96.88 (5.65) & 100 (0) \\ Level 4 & 88.36 (6.68) & 99.07 (3.20) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Task switching percent (%) for subjects and model data just after the ongoing subtask, when the alternative task still remained. Figure 6: Task switching percentage for subjects and model data just after the ongoing subtask, when the alternative task still remained. The results related to Hypothesis 3, which indicate that there exists a significant difference in the performance time of a small unit of a task for selecting and clicking a card in a subtask in the memory game, depending on whether or not task switching occurs while performing the subtask, are shown in Figure 8 and Table 5. We performed a three-way ANOVA to examine whether Hypothesis 3 is statistically correct, and the results illustrate that the difficulty level of the memory game (\(p<0.0001\)) and whether or not task switching occurred during the subtask (\(p<0.0001\)) had a significant effect on the performance time in a small unit of the task of selecting and clicking a card. However, the condition of the subject or model (\(p=0.085\)) had no significant effect on the performance time in the task of selecting and clicking a card. Therefore, we can infer that the model appropriately represents the phenomenon in which the performance time increases in the subtask where task switching occurred, which can be observed in the subject data. However, as shown in Figure 8, there is a difference between subject data and model data, whereby the performance time of the subjects was longer than that of the model at difficulty level 1.