# Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?
## 5 Study 2
### Results  
For discomfort and competence, we used the averaged scores of the ratings for three questions, respectively ([PERSON]'s \(\alpha\) = 0.733 and 0.780). To compare the effects of fillers, we used mean ratings over conversation types per filler condition per participant in this analysis. We performed Friedman tests on each subjective measure and used Wilcoxon  
Figure 7: The experimental setup. -sized-rank tests for pairwise comparisons with Bonferroni adjustment applied to the p-values. The significance level was set to 0.05 for all statistical analyses. Our results are summarized in Table 3 and Figure 8.  
We found statistically significant main effects of filler types on all measures. We present the summarized results of pairwise comparisons in connection with the hypotheses in the following itemized list:  
* User-perceived latency : There were significant differences between NF and other fillers (CF, GF, and GCF > NF, in all \(p\) < 0.001), supporting H1. We also found a significant difference between CF and GCF (\(p\) = 0.036). However, the results did not reveal any statistical differences between GF and GCF and between GF and CF (See Figure 8a and Table 3). * Behavioral naturalness : Pairwise comparisons revealed that there were statistically significant differences between NF and GF (\(p\) < 0.001), NF and GCF (\(p\) < 0.001), CF and GF (\(p\) < 0.001), and CF and GCF (\(p\) < 0.001), supporting H2. No statistical differences were found between NF and CF and GF and GCF (See Figure 8b and Table 3). * Willingness : We found significant differences between NF and other conditions (\(p\) = 0.025, \(p\) < 0.001, \(p\) < 0.001, respectively, in the order of CF, GF, GCF), between CF and GF (\(p\) = 0.004), and between CF and GCF (\(p\) = 0.021). There was no statistical difference between GF and GCF. These results contradict our hypothesis H3 (See Figure 8c and Table 3). * Impression : Our post hoc tests indicated statistically significant differences between NF and GF (\(p\) < 0.001), NF and GCF (\(p\) < 0.001), CF and GF (\(p\) < 0.001), and CF and GCF (\(p\) = 0.011). We, however, did not find differences between NF and CF and GF and GCF. The results for impression also do not support H3 (See Figure 8d and Table 3). * Discomfort : Statistically significant differences were found between NF and GF (\(p\) < 0.001), NF and GCF (\(p\) = 0.031), and CF and GF (\(p\) = 0.001). In general, NF and CF received higher scores compared to GF and GCF, partially supporting H4. We did not find any statistically significant differences between NF and CF, CF and GCF, and GF and GCF (See Figure 8e and Table 3). * Competence : Although we did not set any hypotheses on competence with regard to filler conditions, our results revealed statistically significant differences between NF and GF (\(p\) < 0.001), NF and GCF (\(p\) < 0.001), CF and GF (\(p\) = 0.004), and CF and GCF (\(p\) = 0.022). In general, participants considered digital humans competent for the conditions accompanying gestural fillers (See Figure 8f and Table 3). With regard to the participants' gaze behavior during the delayed responses, one of the experimenters went over the recordings frame by frame and calculated the average time looking at the digital human per each filler condition. Although the nature of the time is an interval, our data failed to pass the Shapiro-Wilk normality test; thus, we decided to perform a non-parametric Friedman test for the gaze behavior. The Friedman test revealed a statistically significant main effect of filler types on gaze behavior (\(\chi^{2}\) = 28.809, \(p\) < 0.001). The Wilcoxon signed-rank test with Bonferroni correction performed on each pair showed statistically significant differences between NF and GF (\(p\) < 0.001), NF and GCF (\(p\) < 0.001), CF and GF (\(p\) = 0.004), and CF and GCF (\(p\) = 0.017). However, statistically significant differences between NF and CF and GF and GCF were not found (see Figure 9).