Spatial limitations in averaging social cues

Abstract

The direction of social attention from groups provides stronger cueing than from an individual. It has previously been shown that both basic visual features such as size or orientation and more complex features such as face emotion and identity can be averaged across multiple elements. Here we used an equivalent noise procedure to compare observers’ ability to average social cues with their averaging of a non-social cue. Estimates of observers’ internal noise (uncertainty associated with processing any individual) and sample-size (the effective number of gaze-directions pooled) were derived by fitting equivalent noise functions to discrimination thresholds. We also used reverse correlation analysis to estimate the spatial distribution of samples used by participants. Averaging of head-rotation and cone-rotation was less noisy and more efficient than averaging of gaze direction, though presenting only the eye region of faces at a larger size improved gaze averaging performance. The reverse correlation analysis revealed greater sampling areas for head rotation compared to gaze. We attribute these differences in averaging between gaze and head cues to poorer visual processing of faces in the periphery. The similarity between head and cone averaging are examined within the framework of a general mechanism for averaging of object rotation.

Cross-cultural and hemispheric laterality effects on the ensemble coding of emotion in facial crowds

“…The key idea here is that the visual system exploits redundancies and regularities in scenes to extract ensemble representations from groups of similar items, without having to examine each individual object. Recent work on ensemble coding has demonstrated human observers’ remarkable ability to extract average emotion (also termed “crowd emotion”) from sets of faces (e.g., Elias, Dyer, & Sweeny, 2016; Fischer & Whitney, 2011; Haberman, Harp, & Whitney, 2009; Haberman & Whitney, 2007; Hubert-Wallander & Boynton, 2015; Im et al, in press; Ji, Chen, & Fu, 2014; Yang et al, 2013), facial identity (de Fockert & Wolfenstein, 2009; Haberman & Whitney, 2007; Leib et al, 2012; Leib et al, 2014; Neumann, Schweinberger, & Burton, 2013), as well as a crowd’s movements (Brunyé, Howe, & Mahoney, 2014; Sweeny, Haroz, & Whitney, 2012) and eye gaze direction (Florey et al, 2016; Sweeny & Whitney, 2014). …”

Differential hemispheric and visual stream contributions to ensemble coding of crowd emotion

“…Therefore, we suggest that extracting crowd emotion relies on a parallel, global process, rather than on a sequential sampling of individual members 104 . In various feature dimensions, the notion of global averaging has been previously tested, by using empirical approaches showing that multiple stimuli were integrated 22 , ideal observer analysis 3,15 , equivalent noise 26 , or general linear modeling 17 . Consistent with this prior work, the current findings suggest that people do average different facial expressions to make social decisions about facial crowds, and such ensemble coding of crowds of faces is achieved via a distinct mechanism from that supporting individual object processing.…”

“…Ensemble coding provides precise global representation 1,7,8,10 , with little or no conscious perception 6,7,11–13 or sampling of individual members in a set 14,15 . Recent work has further shown that ensemble coding occurs for even more complex objects, such as averaging emotion from sets of faces 2,3,16–19 , facial identity 16,20–23 , as well as a crowd’s movements 24,25 and gaze direction 26,27 .…”

The center of attention: Metamers, sensitivity, and bias in the emergent perception of gaze

“…The faces in our investigation were much smaller by comparison (1.01° × 0.84°) and were available for unlimited inspection. These kinds of differences could reasonably be expected to influence the relative weighting of head and eyes in the computation of gaze, particularly if they impact the visibility or salience of these features (Florey, Dakin, Clifford, & Mareschal, 2015; Florey, Clifford, Dakin, & Mareschal, 2016; Gamer & Hecht, 2007). For example, information from the head might be weighted more heavily when a face makes a small image on the viewer’s retina, as when seen from a great distance.…”