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, 9 (1), 11924

Facial Pre-Touch Space Differentiates the Level of Openness Among Individuals

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Facial Pre-Touch Space Differentiates the Level of Openness Among Individuals

Soheil Keshmiri et al. Sci Rep.

Abstract

Social and cognitive psychology provide a rich map of our personality landscape. What appears to be unexplored is the correspondence between these findings and our behavioural responses during day-to-day life interaction. In this article, we utilize cluster analysis to show that the individuals' facial pre-touch space can be divided into three well-defined subspaces and that within the first two immediate clusters around the face area such distance information significantly correlate with their openness in the five-factor model (FFM). In these two clusters, we also identify that the individuals' facial pre-touch space can predict their level of openness that are further categorized into six distinct levels with a highly above chance accuracy. Our results suggest that such personality factors as openness are not only reflected in individuals' behavioural responses but also these responses allow for a fine-grained categorization of individuals' personality.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Predetermined toucher-evaluator interaction positions. In this setting, the toucher (i.e., T) moves along the positions 0 through 8 and stretches his hand toward the face of the evaluator (i.e., E) who is seated in the middle. The two Kinect V2 sensors mounted behind the evaluator collect the joint and the head positions of the toucher and the evaluator. The location of two Kinect V2 sensors that were mounted behind the evaluators’ seat to automatically track the touchers’ hand and the evaluator’s face positions are visible in this figure.
Figure 2
Figure 2
Facial pre-touch data of all the participants. (A) Distribution of actual facial pre-touch distances (in cm). (B) Distribution of log-transformed facial pre-touch distances. (C) 3D map of facial pre-touch distances in which the individuals’ preferential facial personal space are shown along the z-axis. The schematic diagram of the face direction is shown under this subplot. (D) Akaike (AIC in red) and Bayesian (BIC in blue) information criteria unanimously identify K = 3 as the best number of clusters for facial personal space. Their values are: AIC = [12.034, 12.034, 11.986, 11.991, 11.993] and BIC = [12.034, 12.034, 11.987, 11.991, 11.993]. (E) 3D facial pre-touch distance clusters: C1 (red), C2 (green), and C3 (blue). The schematic diagram of the face direction is shown under this subplot. (F) 2D facial pre-touch distance clusters that maps these distances against their corresponding azimuth angle.
Figure 3
Figure 3
Openness (O) versus pre-touch distance Spearman correlations. (A) Cluster C2 (B) Cluster C3 (C) Bootstrap (10,000 simulation runs) 95.0% confidence intervals (CI) of the Spearman correlation between participants’ facial pre-touch distances and their FFM openness scores. The mean of the bootstrapped correlation coefficients is shown with the yellow line, the 95.0% confidence intervals are the two red lines, and the null hypothesis H0 (i.e., no correlation) is the blue line.
Figure 4
Figure 4
KNN accuracy. (A) Overall performance (i.e., six openness levels combined) and without considering the clusters. (B) Comparison of the accuracy between different openness levels and without considering the clusters. This figure illustrates the distribution of 200 simulation rounds in which we randomly assigned 30.0% of entire data to test set and used the remainder of data for training these models. While splitting the data, we also ensured that a proper proportion of each labels (i.e., 30.0% per label) was assigned to the test set. In this figure, the asterisks mark the significant differences between openness level prediction accuracies.
Figure 5
Figure 5
Bootstrap (10,000 simulation runs) 99.0% confidence intervals (CI) for comparative analysis of the overall (i.e., clusters C2 and C3 combined) KNN accuracy. These subplots correspond to the difference between openness levels (A) 1 vs. 2 (B) 1 vs. 3 (C) 1 vs. 4 (D) 1 vs. 5 (E) 1 vs. 6 (F) 2 vs. 3 (G) 2 vs. 4 (H) 2 vs. 5 (I) 2 vs. 6 (J) 3 vs. 4 (K) 3 vs. 5 (L) 3 vs. 6 (M) 4 vs. 5 (N) 4 vs. 6 (O) 5 vs. 6. For each paired comparison the sample mean difference (i.e., μiμj, i = 1, …, 6, j = 1, …, 6) is shown with the yellow line, the 99.0% confidence intervals are the two red lines, and the null hypothesis H0 (i.e., mean difference is zero) is the blue line. Subplot (M) indicates that the comparative overall KNN performance (i.e., combined C2 and C3) between openness levels 4 and 5 is non-significant.
Figure 6
Figure 6
KNN accuracy. (A) C3 versus C2 in the case of within openness level. The asterisks mark the significant differences between openness level prediction accuracies. (B) Overlaid KNN accuracies for better visualization of the effect in clusters C3 and C2. (C) Precision, recall, and F1-score associated with KNN while predicting different openness level in C3 and C2. Column “Support” refers to the number of each openness levels that were included in each of these clusters’ test sets while testing the KNN predictions. The row “average” indicates the average precision, recall, and F1-score when all levels combined in their respective clusters.
Figure 7
Figure 7
Bootstrap (10,000 simulation runs) 99.0% confidence intervals (CI) for KNN performance on clusters C2 and C3 for paired openness (A) level 1 (B) level 2 (C) level 3 (D) level 4 (E) level 5 (F) level 6. For each paired comparison the sample mean difference (i.e., μC2μC3) is shown with the yellow line, the 99.0% confidence intervals are the two red lines, and the null hypothesis H0 (i.e., mean difference is zero) is the blue line. Whereas KNN performed significantly better in C3 for openness levels 2 (subplot (B)) and 6 (subplot (F)) its accuracy was significantly higher in C2 for openness levels 1 (subplot (A)), 3 (subplot (C)), 4 (subplot (D)), and 5 (subplot (E)).

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