Fast ensemble representations for abstract visual impressions

Abstract

Much of the richness of perception is conveyed by implicit, rather than image or feature-level, information. The perception of animacy or lifelikeness of objects, for example, cannot be predicted from image level properties alone. Instead, perceiving lifelikeness seems to be an inferential process and one might expect it to be cognitively demanding and serial rather than fast and automatic. If perceptual mechanisms exist to represent lifelikeness, then observers should be able to perceive this information quickly and reliably, and should be able to perceive the lifelikeness of crowds of objects. Here, we report that observers are highly sensitive to the lifelikeness of random objects and even groups of objects. Observers' percepts of crowd lifelikeness are well predicted by independent observers' lifelikeness judgements of the individual objects comprising that crowd. We demonstrate that visual impressions of abstract dimensions can be achieved with summary statistical representations, which underlie our rich perceptual experience.

Independence of viewpoint and identity in face ensemble processing

“…For instance, in an early demonstration, Ariely (2001) showed that observers could report the average size of a set of circles fairly accurately but were poor at reporting the size of any one particular circle from the set. This summary representation may partially explain the subjective experience of having a rich and detailed perceptual world despite the inability to accurately process all of it (Cohen, Dennett, & Kanwisher, 2016;Leib, Kosovicheva, & Whitney, 2016), and conforms to the visual system utilizing inference-based mechanisms for fast and efficient processing (Kersten, Mamassian, & Yuille, 2004;Purves, Monson, Sundararajan, & Wojtach, 2014).…”

Logarithmic encoding of ensemble time intervals

“…In a seminal study by Ariel 28 , participants, when asked to identify whether a presented object belonged to a group of similar items, tended to automatically respond with the mean size. Intuitive averaging has been demonstrated for various features in the visual domain 29 , from primary ensembles such as object size 30,31 , color and grayness 32,33 , to high-level ensembles such as facial expression and lifelikeness [34][35][36] . Rather than being confined to the (inherently 'parallel') visual domain, ensemble perception has also been demonstrated for sequentially presented items, such as auditory frequency, tone loudness, tone duration, and weight [37][38][39][40] .…”

Roles of saliency and set size in ensemble averaging

“…It has been previously demonstrated that people can efficiently extract various statistical information form briefly presented multiple items. Summary statistics such as mean and variance can be extracted about various features of individual items from basic sensory dimensions, like orientation (Alvarez & Oliva, 2009;Dakin & Watt, 1997;Morgan, Chubb, & Solomon, 2008;Parkes, Lund, Angelucci, Solomon, & Morgan, 2001;Suárez-Pinilla, Seth, & Roseboom, 2018), size (Ariely, 2001;Chong & Treisman, 2003;Khvostov & Utochkin, 2019;Tokita, Ueda, & Ishiguchi, 2016), color (Bronfman, Brezis, Jacobson, & Usher, 2014;Gardelle & Summerfield, 2011;Maule & Franklin, 2015), to quite complex and high-level dimensions, like facial expression (Haberman, Lee, & Whitney, 2015;Haberman & Whitney, 2007) or animacy (Leib, Kosovicheva, & Whitney, 2016). Interestingly, the efficiency and accuracy of such ensemble representation does not suffer (Ariely, 2001;Chong & Treisman, 2005;Fouriezos, Rubenfeld, & Capstick, 2008;Haberman, Harp, & Whitney, 2009; or even benefits (Chong, Joo, Emmmanouil, & Treisman, 2008;Robitaille & Harris, 2011) from increasing set size, whereas our ability to report individual items quickly degrades with set size (Ariely, 2001;Haberman & Whitney, 2007).…”