## 5 Results  
Our results from the study design described in Section 4 showed clear presence of IB and the efficacy of our approach. The quiz scores from each group, designed to show the evidence of the effectiveness of the corresponding social cue, are compared in Table 1 with the mean and standard deviation of each set score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Pre-Test Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **Mean** & **StdDev** & **Mean** & **StdDev** \\ \hline NC & 2.70 & 1.39 & 2.80 & 1.27 \\ OC & 2.63 & 1.16 & 3.20 & 1.83 \\ LC & 2.36 & 1.22 & 5.06 & 1.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mean and standard deviation of each set score (NC: no cue, OC: oblique cue, and LC: leading cue). Figure 1: Our My Own Cognitive Communication Agent (MOCCA) simulator expressing a quick social cue to give a hint to the user. The one-way ANOVA analysis on this data indicated that there were no significant differences in the scores achieved by all three groups in the Pre-test set of questions in which no social cues are provided. This, thereby, demonstrates the group equivalence and well-balanced group design on the participants. As for the Test set scores, the Test set score of the group OC was higher than that of the group NC, although this distinction was not statistically significant, despite the presentation of hints to the OC group by MOCCA. The effect of social cueing shown in the group LC, however, was more apparent and noticeable. The scores of the group LC were significantly higher compared to those of both groups OC and NC. This results are displayed in Figure 2. We also examined the increase in the scores between quiz sets (Pre-test set vs. Test set) within each group. The increase in the Test set score was significant only in the group LC as displayed in Figure 3.  
Additionally, the post-survey results showed that there was no statistically significant difference found on the likeability of MOCCA among the participants but showed general fondness (average of 3.5 in a 5-level Likert scale) as displayed in the left side of Figure 4. However, the visible trend on the linear increase of likeability implies that the LC can be effective in developing a more interactive and effective social robotic cueing mechanism as the social cueing mechanism becomes more proactive (NC \(<\) OC \(<\) LC), with a big enough participants' size of 93. Figure 3: Comparison of set scores of three groups (NC: no cue, OC: oblique cue, and LC: leading cue). A double asterisk indicates statistically significant difference (\(p<0.01\)). Figure 2: Comparison of group scores in each quiz set (NC: no cue, OC: oblique cue, and LC: leading cue). A double asterisk indicates statistically significant difference (\(p<0.01\)). The participants' response on the trust level, as shown in the right side of Figure 4, shows no statistical difference nor any trend. On the contrary, the distinct decline of trust level on the oblique cue (OC) group shows that the random and unexpected social cue can play a negative role in gaining trust between social human-robot interaction. The third question from the post-survey as depicted in Figure 5 aligns with our anticipations in the cue design as well as the level of trust above. The distribution shows clear difference in the success of cue awareness between OC and LC. The system also tracked and recorded participants' gaze trajectories in real time while they were responding to the questions. Figures 6 and 7 demonstrate that the gaze distributions in the OC and LC groups. In oblique cue (OC) case (Figure 6), the gaze trajectories are distributed over most of the choice buttons but not much on the robotic face, which means the robot's social cueing did not have any implications on the human's attention. However, in the leading cueing (LC) case (Figure 7), it is obvious that the user's attentions were focused mainly on the robot's face and the button toward which the robot focused its leading cue.