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Shape-invariant encoding of dynamic primate facial expressions in human perception
Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While p...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195610/ https://www.ncbi.nlm.nih.gov/pubmed/34115584 http://dx.doi.org/10.7554/eLife.61197 |
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author | Taubert, Nick Stettler, Michael Siebert, Ramona Spadacenta, Silvia Sting, Louisa Dicke, Peter Thier, Peter Giese, Martin A |
author_facet | Taubert, Nick Stettler, Michael Siebert, Ramona Spadacenta, Silvia Sting, Louisa Dicke, Peter Thier, Peter Giese, Martin A |
author_sort | Taubert, Nick |
collection | PubMed |
description | Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition. |
format | Online Article Text |
id | pubmed-8195610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-81956102021-06-14 Shape-invariant encoding of dynamic primate facial expressions in human perception Taubert, Nick Stettler, Michael Siebert, Ramona Spadacenta, Silvia Sting, Louisa Dicke, Peter Thier, Peter Giese, Martin A eLife Neuroscience Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition. eLife Sciences Publications, Ltd 2021-06-11 /pmc/articles/PMC8195610/ /pubmed/34115584 http://dx.doi.org/10.7554/eLife.61197 Text en © 2021, Taubert et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Taubert, Nick Stettler, Michael Siebert, Ramona Spadacenta, Silvia Sting, Louisa Dicke, Peter Thier, Peter Giese, Martin A Shape-invariant encoding of dynamic primate facial expressions in human perception |
title | Shape-invariant encoding of dynamic primate facial expressions in human perception |
title_full | Shape-invariant encoding of dynamic primate facial expressions in human perception |
title_fullStr | Shape-invariant encoding of dynamic primate facial expressions in human perception |
title_full_unstemmed | Shape-invariant encoding of dynamic primate facial expressions in human perception |
title_short | Shape-invariant encoding of dynamic primate facial expressions in human perception |
title_sort | shape-invariant encoding of dynamic primate facial expressions in human perception |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195610/ https://www.ncbi.nlm.nih.gov/pubmed/34115584 http://dx.doi.org/10.7554/eLife.61197 |
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