The pathogen-prevalence hypothesis postulates that collectivism would be strengthened in the long term in tandem with recurrent attacks of infectious diseases. However, it is unclear whether a one-time pathogen epidemic would elevate collectivism. The outbreak of COVID-19 and the widespread prevalence of online social networks have provided researchers an opportunity to explore this issue. This study sampled and analyzed the posts of 126,165 active users on Weibo, a leading Chinese online social network. It used independent-sample t-tests to examine whether COVID-19 (...) had an impact on Chinese collectivistic value-related behaviors by comparing the usage frequency of personal pronouns, group-related words, and relationship-related words before and after the outbreak. Overall, most collectivist words exhibited a significant upward trend after the outbreak. In turn, this tendency pointed to a rising sense of collectivism. Hence, this study confirmed the pathogen-prevalence hypothesis in real settings, finding that an outbreak of an infectious disease such as COVID-19 could exert an impact on collectivism and may deliver a theoretical basis for psychological protection against the threat of COVID-19. However, further evaluation is required to ascertain whether this trend is universal or culture-specific. (shrink)

In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the (...) decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis. (shrink)

Confucian culture has always been the most glorious component of Chinese culture. Governing the mainstream world of China for more than two millennia, it has cast a profound and long-lasting influence on the way of thinking and cultural-psychological formation of the Chinese people. Confucianism emphasizes caring about others with benevolence and governing a state with ethics, reflecting the importance of moral principles for politics. “Ren” and “Li” are important parts of the core values of Confucianism, so analyzing the differences between (...) them and their evolution is of great significance for further understanding Confucian culture. This paper selected 132 classic Confucian works from SikuQuanshu, a large collection of books compiled during the Qianlong’s reign of the Qing Dynasty, to calculate the use of frequency of “Ren” and “Li” in those books by means of big data. Then the data was analyzed to show the development trajectory of “Ren” and “Li” from the Spring and Autumn period to the Qing Dynasty, providing a new perspective for the study of Confucian culture. The analysis result shows that from the Spring and Autumn period to the Qing Dynasty, both the frequencies of “Ren” and “Li” record a peak and a bottom: “Ren” has its peak in the Sui and Tang period while “Li” reaches its climax in the Wei and Jin period ; both “Ren” and “Li” hit their bottom during the Yuan Dynasty. The average frequency of “Li” is higher than that of “Ren” during most of the time. In general, “Li” is more frequently referred to in classic Confucian works than “Ren,” especially in those of the pre-Sui and Tang era. The An-Shi Disturbances in the Tang Dynasty may mark an important turning point for the frequencies of “Ren” and “Li” in classic Confucian works. (shrink)

As traditional methods such as questionnaires for measuring risk propensity are not applicable in some scenarios, a nonintrusive method that could automatically identify individuals' risk propensity could be valuable. This study utilized Defense of the Ancients 2 single match data and historical statistics to train predictive models to identify risk propensity by machine learning methods. Self-reported risk propensity scores from 218 DOTA 2 players were paired with their behavioral metrics. The best-performing model occurred with Gaussian process regression. The root mean (...) square error of this model was 1.10, the correlation between predicted scores and self-reported questionnaire scores was 0.44, the R-squared was 0.17, and the test–retest reliability was 0.67. We discussed how selected behavioral features could contribute to predicting risk propensity and how the approach could be of potential value in the application of perceiving individuals' risk propensities. Moreover, the limitations of our study were discussed, and recommendations were made for future studies in this field. (shrink)

Background: Trait self-esteem reflects stable self-evaluation, and it affects social interaction patterns. However, whether and how trait self-esteem can be expressed through behaviors are controversial. Considering that facial expressions can effectively convey information related to personal traits, the present study investigated the three-dimensional facial movements related to self-esteem level and the sex differences therein. Methods: The sample comprised 238 participants. Their levels of trait self-esteem were evaluated by employing the Rosenberg Self-Esteem Scale. During self-introductions, their facial movements in 3D space (...) were recorded by Microsoft Kinect. Two-way ANOVA was performed to analyze the effect of self-esteem and gender on 3D facial movements. Additionally, Logistic regression models were established to describe the relationship between 3D facial movements and self-esteem levels in both genders. Results: The results of two-way ANOVA revealed a main effect of trait self-esteem level for cheeks and lips’ movements. Meanwhile, there was a significant interaction between trait self-esteem and gender on the variability of lips’ movements. In addition, the combination of facial movements can effectively identify trait self-esteem in men and women, with 75.5 and 68% accuracy, respectively. Conclusion: The present results suggest that the 3D facial expressions of individuals with different trait self-esteem levels were different, and such difference is affected by gender. Our study explores a possible way in which trait self-esteem plays a role in social interaction and also provides the basis for automatic self-esteem recognition. (shrink)