## 11 Discussion  
This study investigated the feasibility of evaluating the state of intrinsic motivation by combining non-contact measurements of physiological responses and facial expressions. The proposed system achieved modest performance in the classification of high- and low-motivation states. Feature selection resulted in the consistent retention of the mean RR interval and the intensity of expressions of happiness. The heart rate variability indices reflect the activity of the autonomic nervous system [46; 47; 48], which in turn is deeply involved in the dopaminergic neural system [49], underlying the regulation of intrinsic motivational states [50; 51]. In particular, all measures of autonomic activation are calculated from the mean RR interval, and the mean RR interval contains much information related to the autonomic nervous system. Therefore, it is not surprising that the mean RR interval was retained as predictive features by both SFS and RFE. Statistical analysis between high- and low-motivation states showed a longer RR interval, although not statistically significant, in the high-motivation state than in the low-motivation state, indicating that reduced sympathetic activation constitutes one as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Threshold** & **Accuracy Rate** & **Specificity** & **AUC** & **F1** \\ \hline \hline (a) Logistic Regression & & & & & \\ \hline train & 0.677 & 0.730 & 0.897 & 0.728 & 0.707 \\ test & 0.687 & 0.740 & 0.882 & 0.680 & 0.725 \\ \hline (b) Random Forest & & & & & \\ \hline train & 0.404 & 0.990 & 0.985 & 0.998 & 0.990 \\ test & 0.404 & 0.780 & 0.853 & 0.788 & 0.778 \\ \hline (c) SVC & & & & & \\ \hline train & 0.394 & 0.940 & 0.956 & 0.973 & 0.940 \\ test & 0.364 & 0.720 & 0.765 & 0.699 & 0.724 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification performance metrics for classifiers using two features selected both by SFS and RFE. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Threshold** & **Accuracy Rate** & **Specificity** & **AUC** & **F1** \\ \hline (a) Logistic Regression & & & & & \\ \hline train & 0.586 & 0.690 & 0.750 & 0.733 & 0.693 \\ test & 0.667 & 0.720 & 0.853 & 0.686 & 0.708 \\ \hline (b) Random Forest & & & & & \\ \hline train & 0.404 & 1.000 & 1.000 & 1.000 & 1.000 \\ test & 0.434 & 0.740 & 0.853 & 0.760 & 0.732 \\ \hline (c) SVC & & & & & \\ \hline train & 0.394 & 0.760 & 0.853 & 0.517 & 0.755 \\ test & 0.343 & 0.640 & 0.618 & 0.671 & 0.652 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification performance metrics for classifiers using RFE. pect of a highly motivational state. A traditional theory [52] has linked high levels of physiological arousal to stress, while a moderate level of physiological arousal has been linked to the optimal state of the so-called \"flow state\", characterized by focused attention and full absorption in an ongoing activity. Thus, a numerically longer RR interval (hence, lower physiological arousal) during the high-motivation state raises the possibility that participants were more likely to have experienced the flow state during a high- rather than low-motivation state, while they tended to experience more stress during the low-motivation state. This interpretation dovetails the claim that the flow state experienced during an activity is associated with the intrinsic motivation to engage in and complete the activity [52; 53]. After feature selection, the intensity of happiness was retained in both SFS and RFE, and contempt was retained in RFE. The classifier with the best performance (random forest using features selected by both SFS and RFE) used the intensity of happiness as a predictive feature. Though not statistically significant, a happy expression was numerically stronger in the high- rather than low-motivation state. This finding is consistent with previous studies linking positive emotions to intrinsic motivation [51]. In addition, it has been shown that smiling is an important signal of intrinsic motivation in terms of facial expressions [54].