The image features of emotional faces that predict the initial eye movement to a face

Abstract Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is...

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Autores principales:	S. M. Stuit, T. M. Kootstra, D. Terburg, C. van den Boomen, M. J. van der Smagt, J. L. Kenemans, S. Van der Stigchel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eb8e5a2e936c4876b78613f7a129cfc6
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spelling	oai:doaj.org-article:eb8e5a2e936c4876b78613f7a129cfc62021-12-02T14:30:52ZThe image features of emotional faces that predict the initial eye movement to a face10.1038/s41598-021-87881-w2045-2322https://doaj.org/article/eb8e5a2e936c4876b78613f7a129cfc62021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87881-whttps://doaj.org/toc/2045-2322Abstract Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.S. M. StuitT. M. KootstraD. TerburgC. van den BoomenM. J. van der SmagtJ. L. KenemansS. Van der StigchelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q S. M. Stuit T. M. Kootstra D. Terburg C. van den Boomen M. J. van der Smagt J. L. Kenemans S. Van der Stigchel The image features of emotional faces that predict the initial eye movement to a face
description	Abstract Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.
format	article
author	S. M. Stuit T. M. Kootstra D. Terburg C. van den Boomen M. J. van der Smagt J. L. Kenemans S. Van der Stigchel
author_facet	S. M. Stuit T. M. Kootstra D. Terburg C. van den Boomen M. J. van der Smagt J. L. Kenemans S. Van der Stigchel
author_sort	S. M. Stuit
title	The image features of emotional faces that predict the initial eye movement to a face
title_short	The image features of emotional faces that predict the initial eye movement to a face
title_full	The image features of emotional faces that predict the initial eye movement to a face
title_fullStr	The image features of emotional faces that predict the initial eye movement to a face
title_full_unstemmed	The image features of emotional faces that predict the initial eye movement to a face
title_sort	image features of emotional faces that predict the initial eye movement to a face
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eb8e5a2e936c4876b78613f7a129cfc6
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The image features of emotional faces that predict the initial eye movement to a face

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