Summary statistics (also referred to as ensemble coding or set representations) have been discussed generally in the context of experiments displaying simple stimuli as circles (Ariely,
2001; Corbett & Oriet,
2011; see
Figure 1A,
1B), Gabor patches (Attarha & Moore,
2015a) and colored forms (Ward, Bear, & Scholl,
2016). These tested, correspondingly, coded information of mean size (Ariely,
2001; Corbett & Oriet,
2011; Allik, Toom, Raidvee, Averin, & Kreegipuu,
2014), orientation (Alvarez & Oliva,
2009), and hue (Maule & Franklin,
2015; Ward et al.,
2016), as well as brightness (Bauer,
2009), spatial position (Alvarez & Oliva,
2008), and motion speed and direction (Sweeny, Haroz, & Whitney,
2013). Most studies used static arrays (Ariely,
2001; Chong & Treisman,
2003,
2005), in which set stimuli were presented simultaneously, testing spatial summation (Alvarez & Oliva,
2009). Nevertheless, visual input in real life is dynamic (Hubert-Wallander & Boynton,
2015), and other studies used rapid visual serial presentation (RSVPs), requiring summarizing over time (Corbett & Oriet,
2011; Brezis, Bronfman, & Usher,
2015; Hubert-Wallander & Boynton,
2015). With either presentation mode, results show that observers estimate mean set properties quite precisely and performance is speeded with increased number of display items (Corbett & Oriet,
2011; Robitaille & Harris,
2011). In contrast, observer ability to identify set member items was close to chance, indicating that they coded very little information of individuals but still possessed information concerning the set as a whole (Ariely,
2001; Corbett & Oriet,
2011; Utochkin,
2015; Ward et al.,
2016).