Literature DB >> 26676106

Mixed emotions: Sensitivity to facial variance in a crowd of faces.

Jason Haberman, Pegan Lee, David Whitney.   

Abstract

The visual system automatically represents summary information from crowds of faces, such as the average expression. This is a useful heuristic insofar as it provides critical information about the state of the world, not simply information about the state of one individual. However, the average alone is not sufficient for making decisions about how to respond to a crowd. The variance or heterogeneity of the crowd--the mixture of emotions--conveys information about the reliability of the average, essential for determining whether the average can be trusted. Despite its importance, the representation of variance within a crowd of faces has yet to be examined. This is addressed here in three experiments. In the first experiment, observers viewed a sample set of faces that varied in emotion, and then adjusted a subsequent set to match the variance of the sample set. To isolate variance as the summary statistic of interest, the average emotion of both sets was random. Results suggested that observers had information regarding crowd variance. The second experiment verified that this was indeed a uniquely high-level phenomenon, as observers were unable to derive the variance of an inverted set of faces as precisely as an upright set of faces. The third experiment replicated and extended the first two experiments using method-of-constant-stimuli. Together, these results show that the visual system is sensitive to emergent information about the emotional heterogeneity, or ambivalence, in crowds of faces.

Mesh:

Year:  2015        PMID: 26676106     DOI: 10.1167/15.4.16

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  9 in total

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Journal:  Atten Percept Psychophys       Date:  2021-03-16       Impact factor: 2.199

3.  Serial dependence in the perception of visual variance.

Authors:  Marta Suárez-Pinilla; Anil K Seth; Warrick Roseboom
Journal:  J Vis       Date:  2018-07-02       Impact factor: 2.240

4.  Variability leads to overestimation of mean summaries.

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Journal:  Atten Percept Psychophys       Date:  2021-03-26       Impact factor: 2.199

5.  People perception and stereotype-based responding: task context matters.

Authors:  Linn M Persson; Johanna K Falbén; Dimitra Tsamadi; C Neil Macrae
Journal:  Psychol Res       Date:  2022-08-22

6.  The Representational Dynamics of Sequential Perceptual Averaging.

Authors:  Jongrok Do; Kang Yong Eo; Oliver James; Joonyeol Lee; Yee-Joon Kim
Journal:  J Neurosci       Date:  2021-12-13       Impact factor: 6.709

Review 7.  Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review.

Authors:  Lucy Cui; Zili Liu
Journal:  Atten Percept Psychophys       Date:  2021-01-03       Impact factor: 2.199

8.  Emotional judgments of scenes are influenced by unintentional averaging.

Authors:  Yavin Alwis; Jason M Haberman
Journal:  Cogn Res Princ Implic       Date:  2020-06-11

9.  Temporal bisection is influenced by ensemble statistics of the stimulus set.

Authors:  Xiuna Zhu; Cemre Baykan; Hermann J Müller; Zhuanghua Shi
Journal:  Atten Percept Psychophys       Date:  2020-11-26       Impact factor: 2.199

  9 in total

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