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A data-driven, hyper-realistic method for visualizing individual mental representations of faces

Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., “X type of face looks trustworthy”). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level varia...

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Detalles Bibliográficos
Autores principales: Albohn, Daniel N., Uddenberg, Stefan, Todorov, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554410/
https://www.ncbi.nlm.nih.gov/pubmed/36248585
http://dx.doi.org/10.3389/fpsyg.2022.997498
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author Albohn, Daniel N.
Uddenberg, Stefan
Todorov, Alexander
author_facet Albohn, Daniel N.
Uddenberg, Stefan
Todorov, Alexander
author_sort Albohn, Daniel N.
collection PubMed
description Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., “X type of face looks trustworthy”). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method.
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spelling pubmed-95544102022-10-13 A data-driven, hyper-realistic method for visualizing individual mental representations of faces Albohn, Daniel N. Uddenberg, Stefan Todorov, Alexander Front Psychol Psychology Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., “X type of face looks trustworthy”). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9554410/ /pubmed/36248585 http://dx.doi.org/10.3389/fpsyg.2022.997498 Text en Copyright © 2022 Albohn, Uddenberg and Todorov. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Albohn, Daniel N.
Uddenberg, Stefan
Todorov, Alexander
A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title_full A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title_fullStr A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title_full_unstemmed A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title_short A data-driven, hyper-realistic method for visualizing individual mental representations of faces
title_sort data-driven, hyper-realistic method for visualizing individual mental representations of faces
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554410/
https://www.ncbi.nlm.nih.gov/pubmed/36248585
http://dx.doi.org/10.3389/fpsyg.2022.997498
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