September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Emotional judgments of individual scenes are influenced by unintentional averaging
Author Affiliations
  • Yavin Alwis
    Neuroscience Program in the Department of Psychology, Rhodes College
  • Jason Haberman
    Neuroscience Program in the Department of Psychology, Rhodes College
Journal of Vision September 2018, Vol.18, 89. doi:10.1167/18.10.89
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      Yavin Alwis, Jason Haberman; Emotional judgments of individual scenes are influenced by unintentional averaging. Journal of Vision 2018;18(10):89. doi: 10.1167/18.10.89.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The visual system uses ensemble perception to summarize visual input across a variety of domains. This heuristic operates at the highest levels of vision, compressing information as complex as emotion, animacy, and scene valence into an efficient representation. Previous work has shown the average size of a set can influence the perceived size of an individual item, and vice versa (Brady & Alvarez, 2011), but it has yet to be demonstrated whether such effects emerge for high-level stimuli, specifically, emotional scenes. In the current experiment, we tested whether the perceived emotional valence of a single scene could be influenced by surrounding, simultaneously presented scenes. Observers first rated the emotional valence of a series of individual scenes. Observers then saw ensembles of the original images and were cued to rerate just one of the four. We predicted the perceived emotional valence of the cued image would be pulled toward the mean emotion of the surrounding ensemble. Results confirmed this. The correlations across observers revealed that the bigger the difference between the valence of the ensemble and the valence of the cued image, the more the rating of the cued image shifted toward the average of the ensemble. We conclude that high-level ensemble information can influence how we perceive individual items in the crowd, even when that information is not directly task relevant.

Meeting abstract presented at VSS 2018

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