Revealing an Unattractivity Bias in Mental Reconstruction of Occluded Faces using Generative Image Models

Riedmann F, Egger B, Rohe T (2024)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2024

Event location: Boston US

URI: https://2024.ccneuro.org/pdf/154_Paper_authored_CCN2024_MaskedFaces-Authored.pdf

Open Access Link: https://2024.ccneuro.org/pdf/154_Paper_authored_CCN2024_MaskedFaces-Authored.pdf

Abstract

Previous studies have shown that faces are rated as more
attractive when they are partially occluded. The cause of
this observation remains unclear. One explanation is a
mental reconstruction of the occluded face parts which
is biased towards a more attractive percept as shown in
face-attractiveness rating tasks. We aimed to test for this
hypothesis by using a delayed matching-to-sample task,
which directly requires mental reconstruction. In two on-
line experiments, we presented observers with unattrac-
tive, neutral or attractive synthetic reconstructions of
the occluded face parts using a state-of-the-art diffusion-
based image generator. Our experiments do not support
the initial hypothesis and reveal an unattractiveness bias
for occluded faces instead. This suggests that facial at-
tractiveness rating tasks do not prompt reconstructions.
Rather, the attractivity bias may arise from global image
features, and faces may actually be reconstructed with
unattractive properties when mental reconstruction is ap-
plied.

Authors with CRIS profile

Bernhard Egger Professur für Cognitive Computer Vision (Stiftungsprofessur) Tim Rohe Juniorprofessur für Wahrnehmungspsychologie

How to cite

APA:

Riedmann, F., Egger, B., & Rohe, T. (2024). Revealing an Unattractivity Bias in Mental Reconstruction of Occluded Faces using Generative Image Models. In Proceedings of the Cognitive Computational Neuroscience. Boston, US.

MLA:

Riedmann, Frederik, Bernhard Egger, and Tim Rohe. "Revealing an Unattractivity Bias in Mental Reconstruction of Occluded Faces using Generative Image Models." Proceedings of the Cognitive Computational Neuroscience, Boston 2024.

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