Ensemble statistics as units of selection

Abstract

The current study investigated whether attentional mechanisms operate on ensembles as higher-order units for selection. In Experiment 1, we presented sets of circles and asked participants to compare the mean sizes of the sets while concurrently detecting a small probe appearing at a centroid of one of the sets. We found that, both with and even without the task instruction to favour larger mean sizes, people's mean size judgement was more accurate for the sets with larger mean sizes. In addition, detection of the probe appearing in the set with the largest mean size was facilitated by a matching task instruction. However, when the task instruction favoured smaller mean sizes, mean size judgement became more accurate for the sets with smaller mean sizes. These results suggest that attentional selection can be based on ensembles. In Experiment 2, we found further evidence that attention was directed towards the centroid of an ensemble, rather than towards an individual member of the ensemble. Together, these results suggest that attentional modulation can operate at the level of ensembles instead of selecting individuals separately and that the centroid of an ensemble can be the locus of selection based on an ensemble.

Effects of ensemble complexity and perceptual similarity on rapid averaging of hue

“…This proposition is also supported by evidence for adaptation to the mean of an ensemble (Corbett et al, 2012), evidence that ensemble means may be used as ''units'' of working memory Im, Park, & Chong, 2014) and are represented across multiple frames of reference (Corbett & Melcher, 2014a). Recent evidence also suggests that ensemble means can be used by 4-to 5-year-olds to guide their perceptual judgments (Sweeny, Wurnitsch, Gopnik, & Whitney, 2014), adding weight to the argument that ensemble coding is a pervasive feature of the visual system, and in some cases, averaging appears to be an obligatory process (e.g., Allik et al, 2014;Parkes et al, 2001), requiring little or no attention to the features in question (Alvarez & Oliva, 2008Baijal et al, 2013).…”

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

“…Global information extracted via ensemble coding influences processing of individual objects in many different ways. Because ensemble coding compresses properties of multiple objects into a compact description with a higher level of abstraction 108 , it allows observers to surmount the severe limitations on individual object processing 109–113 , imposed by attention or working memory 114–117 . Furthermore, the global information of ensembles allows for an initial, rough analysis of visual inputs, which then biases and facilitates the processing of individual objects 108,109,113,118 .…”

“…Because ensemble coding compresses properties of multiple objects into a compact description with a higher level of abstraction 108 , it allows observers to surmount the severe limitations on individual object processing 109–113 , imposed by attention or working memory 114–117 . Furthermore, the global information of ensembles allows for an initial, rough analysis of visual inputs, which then biases and facilitates the processing of individual objects 108,109,113,118 . For example, extracted ensemble representation influences the individual object processing, by guiding detection of outliers in a set (e.g., pop-out visual search 108,118 ), facilitating selection of an individual object at the center location of a set 113 , and biasing memory for individual objects towards the global mean 119 .…”

“…Furthermore, the global information of ensembles allows for an initial, rough analysis of visual inputs, which then biases and facilitates the processing of individual objects 108,109,113,118 . For example, extracted ensemble representation influences the individual object processing, by guiding detection of outliers in a set (e.g., pop-out visual search 108,118 ), facilitating selection of an individual object at the center location of a set 113 , and biasing memory for individual objects towards the global mean 119 . Our results from the whole brain and the ROI analyses both support our hypothesis that the dorsal visual stream contributes to global processing for crowd emotion extraction whereas the ventral visual stream contributes more to object-based (local) processing of emotion in individual faces.…”

Ensemble coding of crowd emotion: Differential hemispheric and visual stream contributions

“…Global information extracted via ensemble coding influences processing of individual objects in many different ways. Because ensemble coding compresses properties of multiple objects into a compact description with a higher level of abstraction (Utochkin, 2015), it allows observers to surmount the severe limitations on individual object processing Cohen, Dennett, & Kanwisher, 2016;Feigenson, 2011;Im & Chong, 2014;Im, Park, & Chong, 2015), imposed by attention or working memory (e.g., Cowan, 2001;Luck & Vogel, 1997;Pylyshyn & Storm, 1988;Treisman & Gelade, 1980). Furthermore, the global information of ensembles allows for an initial, rough analysis of visual inputs, which then biases and facilitates the processing of individual objects (Brady & Alvarez, 2011;Haberman & Whitney, 2012;Im et al, 2015;Utochkin 2015).…”