Ensemble perception of faces with naturalistic occlusions

The visual system takes advantage of redundancy in the world by extracting summary statistics, a phenomenon known as ensemble perception. Ensemble representations are formed for low-level features like orientation and size and high-level features such as facial identity and expression. Whereas recent research has shown that the visual system forms intact ensemble representations even when faces are partially occluded via solid bars, how ensemble perception is impacted with the addition of naturalistic objects such as face masks or sunglasses is largely unknown. To investigate this, we conducted a series of experiments using continuous report tasks in which faces (either varying in identity or expression) were partially occluded with a surgical mask or sunglasses and participants had to report the average face using a face wheel. We found evidence that participants could still accurately extract the average even when a significant portion of it was occluded with either face masks or sunglasses. In a second experiment, however, we found performance was worse when the face wheel was variable trial to trial. Thus part of the preservation of performance in occlusion arises from the visual system learning the features of the particular face wheel being used. Overall, our results suggest that the visual system is able to establish robust ensemble representations for faces with naturalistic occlusions, but that robustness appears to be supported at least partially by learning information about the particular features that are informative for a given set of faces.

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Publication Details

Journal
Journal of Vision
Published
2025-08-08
DOI
https://doi.org/10.1167/jov.25.10.5
Primary Topic
Face Recognition and Perception
Type
article
Field-Weighted Citation Impact
0.00

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article

Ensemble perception of faces with naturalistic occlusions

Timothy F. Brady, Hayden Schill Hendley, Natalia K. Pallis Hassani
Journal of Vision
Face Recognition and Perception
article

Ensemble perception of faces with naturalistic occlusions

Timothy F. Brady, Hayden Schill Hendley, Natalia K. Pallis Hassani
article en

Abstract

The visual system takes advantage of redundancy in the world by extracting summary statistics, a phenomenon known as ensemble perception. Ensemble representations are formed for low-level features like orientation and size and high-level features such as facial identity and expression. Whereas recent research has shown that the visual system forms intact ensemble representations even when faces are partially occluded via solid bars, how ensemble perception is impacted with the addition of naturalistic objects such as face masks or sunglasses is largely unknown. To investigate this, we conducted a series of experiments using continuous report tasks in which faces (either varying in identity or expression) were partially occluded with a surgical mask or sunglasses and participants had to report the average face using a face wheel. We found evidence that participants could still accurately extract the average even when a significant portion of it was occluded with either face masks or sunglasses. In a second experiment, however, we found performance was worse when the face wheel was variable trial to trial. Thus part of the preservation of performance in occlusion arises from the visual system learning the features of the particular face wheel being used. Overall, our results suggest that the visual system is able to establish robust ensemble representations for faces with naturalistic occlusions, but that robustness appears to be supported at least partially by learning information about the particular features that are informative for a given set of faces.

Journal of VisionVol. 25(10)
University of California, Riverside (US), Twitter (United States) (US), University of California, Los Angeles (US), Henley College (GB), University of California San Diego (US), Universidad Cristiana de las Asambleas de Dios (SV), Kinetic Art & Technology (United States) (US)
McGill University, University of California, San Diego
Openalex Percentile: Top 80%
Face Recognition and Perception
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