“…The Bayes factor (BF 10 ) was calculated using 10.000 Monte-Carlo sampling iterations; the null hypothesis corresponded to a standardized effect size δ = 0, while the alternative hypothesis was defined as a Cauchy prior distribution centred around 0 with a scaling factor of r = 0.707 (Rouder, Morey, Speckman, & Province, 2012). In line with the Bayes factor interpretation (Jeffreys, 1961;Lee & Wagenmakers, 2013) and with previous studies reporting Bayes factors (Korka et al, 2019;Marzecová et al, 2018;Stuckenberg, Schröger, & Widmann, 2019), data were taken as moderate evidence for the alternative (or null) hypothesis if the BF 10 was greater than 3 (or lower than 0.33), while values close to 1 were considered only weakly informative. Values greater than 10 (or smaller than 0.1) were considered strong evidence for the alternative (or null) hypothesis.…”