Viewers extract the mean from images of the same person: A route to face learning

Abstract

Research on ensemble encoding has found that viewers extract summary information from sets of similar items. When shown a set of four faces of different people, viewers merge identity information from the exemplars into a representation of the set average. Here, we presented sets containing unconstrained images of the same identity. In response to a subsequent probe, viewers recognized the exemplars accurately. However, they also reported having seen a merged average of these images. Importantly, viewers reported seeing the matching average of the set (the average of the four presented images) more often than a nonmatching average (an average of four other images of the same identity). These results were consistent for both simultaneous and sequential presentation of the sets. Our findings support previous research suggesting that viewers form representations of both the exemplars and the set average. Given the unconstrained nature of the photographs, we also provide further evidence that the average representation is invariant to several high-level characteristics.

Ensemble coding of faces occurs in children and develops dissociably from coding of individual faces

“…Scores were numerically higher , not lower , for recognition of individual faces than set averages in all four cases. These results replicate other face identity findings with adults and extend them to children (Kramer et al., ; Neumann et al., ). Therefore, an advantage for recognition of set averages, which is sometimes seen for non‐face stimuli (e.g., Ariely, ), and which is taken as evidence for distinct processes for ensemble and individual coding of other attributes, is not found for face identity.…”

“…Ensemble coding has been reported for many simple visual features, such as size, orientation and direction of motion (for a review, see Whitney et al, 2014). It also occurs for more complex stimuli, such as faces, with ensemble coding reported for identity, expression, gender, attractiveness and gaze direction of groups of faces (e.g., de Fockert & Wolfenstein, 2009;Haberman & Whitney, 2007;Kramer, Ritchie, & Burton, 2015;Neumann, Schweinberger, & Burton, 2013;Walker & Vul, 2014). In some cases, the ensemble information is available in the absence of information about individual faces (e.g., Haberman & Whitney, 2007), whereas in other cases both types of information are available (e.g., Kramer et al, 2015;Neumann et al, 2013).…”

Finding an unfamiliar face in a line‐up: Viewing multiple images of the target is beneficial on target‐present trials but costly on target‐absent trials

“…Our results add to the many studies that suggest that exposure to within‐person variability contributes to face learning (Andrews et al ., ; Bindemann & Sandford, ; Dowsett et al ., ; Menon et al ., 2015b; Ritchie & Burton, ). As we encounter more instances of an unfamiliar face, we form an average representation of their appearance (Burton et al ., ; Kramer, Ritchie, & Burton, ) and store previously encountered instances of that identity (Burton, Kramer, Ritchie, & Jenkins, ; Young & Burton, ), allowing us to represent the range of variability in their appearance and recognize that person in new instances. However, it remains unclear what exactly we are learning as we become familiar with a face.…”

Face averages and multiple images in a live matching task

“…Face averages have also been shown to help with human face recognition when the averages comprise several ambient images (White, Burton, et al., ) and when facial composites are averaged together (Bruce, Ness, Hancock, Newman, & Rarity, ; Hasel & Wells, ). Evidence suggests that people may form an average as an internal representation when shown an array of images of a new identity (Kramer, Ritchie, & Burton, ). However, this reported improvement in matching with face averages was not replicated in other work, where no overall benefit was found in human performance (Ritchie et al ., ).…”