Holistic ensemble perception

Abstract

In a glance, observers can evaluate gist characteristics from crowds of faces, such as the average emotional tenor or the average family resemblance. Prior research suggests that high-level ensemble percepts rely on holistic and viewpoint-invariant information. However, it is also possible that feature-based analysis was sufficient to yield successful ensemble percepts in many situations. To confirm that ensemble percepts can be extracted holistically, we asked observers to report the average emotional valence of Mooney face crowds. Mooney faces are two-tone, shadow-defined images that cannot be recognized in a part-based manner. To recognize features in a Mooney face, one must first recognize the image as a face by processing it holistically. Across experiments, we demonstrated that observers successfully extracted the average emotional valence from crowds that were spatially distributed or viewed in a rapid temporal sequence. In a subsequent set of experiments, we maximized holistic processing by including only those Mooney faces that were difficult to recognize when inverted. Under these conditions, participants remained highly sensitive to the average emotional valence of Mooney face crowds. Taken together, these experiments provide evidence that ensemble perception can operate selectively on holistic representations of human faces, even when feature-based information is not readily available.

Introduction to the special issue on ensemble perception

“…In this special issue, several groups explored ensemble face perception. Han et al (2020) found ensemble coding of holistic representations of faces and Ji and Hayward (2020) found that observers have metacognitive awareness of their explicit ensemble face percepts. Echoing some of the work on faces (e.g., Han et al, 2020), ensemble perception of biological motion speed (Nguyen et al, 2020;Sweeny et al, 2013) also suggests that configural or holistic information can be represented as an ensemble.…”

Conscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making.

“…In a complex visual scene, it seems impossible to perceive everything precisely at a glance because of the limited capacity of our visual system (Cohen et al, 2016; Luck & Vogel, 1997; McClelland & Bayne, 2016). However, we are capable of rapidly accessing the summary statistics, such as the mean and the variance, of the stimulus set in the scene along a variety of dimensions including not only the low-level and middle-level (e.g., orientation, hue, size; see Chetverikov et al, 2017; Dakin & Watt, 1997; Dan, 2001; Hansmann-Roth et al, 2019, 2021; Joo et al, 2009) but also the high-level visual features (e.g., emotional expression, facial identity; see Haberman et al, 2009; Haberman & Whitney, 2007, 2011; Han et al, 2021; Peng et al, 2020). This ability to extract the summary statistics from groups of similar objects is referred to as ensemble perception or summary representation (Alvarez, 2011; Hubert-Wallander & Boynton, 2015; Whitney & Yamanashi Leib, 2018).…”

“…but also the high-level visual features (e.g., emotional expression, facial identity; see Haberman et al, 2009;Haberman & Whitney, 2007, 2011Han et al, 2021;Peng et al, 2020). This ability to extract the summary statistics from groups of similar objects is referred to as ensemble perception or summary representation (Alvarez, 2011;Hubert-Wallander & Boynton, 2015;Whitney & Yamanashi Leib, 2018).…”