... The ability to extract the mean value of a visual feature from a set of items spans across lowlevel features, such as size (e.g., Allik et al., 2013;Ariely, 2001;Corbett et al., 2012;Corbett & Melcher, 2014;Im & Halberda, 2013;Luo & Zhao, 2018;Tiurina & Utochkin, 2019;Tokita et al., 2016), line length (Bauer, 2017;Utochkin, Khvostov, & Stakina, 2018), orientation (J. A. Solomon, 2010;Utochkin et al., 2018;Witt, 2019) and hue (Maule & Franklin, 2015Michael, de Gardelle, & Summerfield, 2014;Tong et al., 2015) to highlevel features, such as emotion and gender (e.g., Haberman & Whitney, 2007, facial expressions (e.g., Griffiths et al., 2018;Li et al., 2016;Wolfe et al., 2015), and lifelikeness (e.g., Yamanashi Leib, Kosovicheva, & Whitney, 2016). Size is perhaps one of the most studied features, and is tested with various stimuli: typically with dots/circles (e.g., Ariely, 2001;Chong & Treisman, 2003) but also with concrete illustrations of strawberries and lollipops (Yang, Tokita, & Ishiguchi, 2018). ...