A Bayesian investigation of interpersonal distance during the first COVID 19 outbreak

Humans typically create and maintain social bonds through interactions occuring at close social distances. The interpersonal distance (IPD) maintained from others is usually shorter [1], [2] than the one of at least 1.5 m recommended for COVID-19 contagion containment [3], [4]. In a web-based experimental study conducted during the first pandemic wave (mid-April 2020), we asked 242 participants from 28 different countries to regulate their preferred IPD towards confederates who did or did not wear protective masks and gloves, and whose COVID-19 test results were positive, negative, or unknown. Information concerning dispositional factors (perceived vulnerability to disease [5], moral attitudes [6], and prosocial tendencies [7]) as well as situational factors (perceived severity of the situation, frequency of physical and virtual social contacts [8], and attitudes towards quarantine [9]) that may modulate compliance with safety prescriptions was also acquired. A Bayesian analysis approachwas adopted. To avoid overfitting and include only the relevant variables, we applied a model selection approach [10]. Multilevel models of increasing complexity were fitted and compared through approximate leave-one-out cross-validation. This was done using Pareto-smoothed importance sampling [10], which estimates out-ofsample predictive accuracy by means of within-sample fits. Analysis of the final model focused on posterior contrasts between all levels of categorical predictors and the slope of continuous predictors. To quantify the uncertainty and magnitude of effects, we computed the 95% highest-density intervals [11]. To quantify evidence for the presence of effects, we calculated the Bayes factors (BF;[12]) and adopted a common rule of thumb, where BF10 > 3 indicates support for the alternative hypothesis and BF10 < 1/3 for the null hypothesis [13]. Results showed that individual differences did not modulate IPD. 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