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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9554410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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