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Using principal component analysis to characterize eye movement fixation patterns during face viewing

Human faces contain dozens of visual features, but viewers preferentially fixate just two of them: the eyes and the mouth. Face-viewing behavior is usually studied by manually drawing regions of interest (ROIs) on the eyes, mouth, and other facial features. ROI analyses are problematic as they requi...

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Autores principales: Wegner-Clemens, Kira, Rennig, Johannes, Magnotti, John F., Beauchamp, Michael S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6833982/
https://www.ncbi.nlm.nih.gov/pubmed/31689715
http://dx.doi.org/10.1167/19.13.2
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author Wegner-Clemens, Kira
Rennig, Johannes
Magnotti, John F.
Beauchamp, Michael S.
author_facet Wegner-Clemens, Kira
Rennig, Johannes
Magnotti, John F.
Beauchamp, Michael S.
author_sort Wegner-Clemens, Kira
collection PubMed
description Human faces contain dozens of visual features, but viewers preferentially fixate just two of them: the eyes and the mouth. Face-viewing behavior is usually studied by manually drawing regions of interest (ROIs) on the eyes, mouth, and other facial features. ROI analyses are problematic as they require arbitrary experimenter decisions about the location and number of ROIs, and they discard data because all fixations within each ROI are treated identically and fixations outside of any ROI are ignored. We introduce a data-driven method that uses principal component analysis (PCA) to characterize human face-viewing behavior. All fixations are entered into a PCA, and the resulting eigenimages provide a quantitative measure of variability in face-viewing behavior. In fixation data from 41 participants viewing four face exemplars under three stimulus and task conditions, the first principal component (PC1) separated the eye and mouth regions of the face. PC1 scores varied widely across participants, revealing large individual differences in preference for eye or mouth fixation, and PC1 scores varied by condition, revealing the importance of behavioral task in determining fixation location. Linear mixed effects modeling of the PC1 scores demonstrated that task condition accounted for 41% of the variance, individual differences accounted for 28% of the variance, and stimulus exemplar for less than 1% of the variance. Fixation eigenimages provide a useful tool for investigating the relative importance of the different factors that drive human face-viewing behavior.
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spelling pubmed-68339822019-11-07 Using principal component analysis to characterize eye movement fixation patterns during face viewing Wegner-Clemens, Kira Rennig, Johannes Magnotti, John F. Beauchamp, Michael S. J Vis Article Human faces contain dozens of visual features, but viewers preferentially fixate just two of them: the eyes and the mouth. Face-viewing behavior is usually studied by manually drawing regions of interest (ROIs) on the eyes, mouth, and other facial features. ROI analyses are problematic as they require arbitrary experimenter decisions about the location and number of ROIs, and they discard data because all fixations within each ROI are treated identically and fixations outside of any ROI are ignored. We introduce a data-driven method that uses principal component analysis (PCA) to characterize human face-viewing behavior. All fixations are entered into a PCA, and the resulting eigenimages provide a quantitative measure of variability in face-viewing behavior. In fixation data from 41 participants viewing four face exemplars under three stimulus and task conditions, the first principal component (PC1) separated the eye and mouth regions of the face. PC1 scores varied widely across participants, revealing large individual differences in preference for eye or mouth fixation, and PC1 scores varied by condition, revealing the importance of behavioral task in determining fixation location. Linear mixed effects modeling of the PC1 scores demonstrated that task condition accounted for 41% of the variance, individual differences accounted for 28% of the variance, and stimulus exemplar for less than 1% of the variance. Fixation eigenimages provide a useful tool for investigating the relative importance of the different factors that drive human face-viewing behavior. The Association for Research in Vision and Ophthalmology 2019-11-05 /pmc/articles/PMC6833982/ /pubmed/31689715 http://dx.doi.org/10.1167/19.13.2 Text en Copyright 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Wegner-Clemens, Kira
Rennig, Johannes
Magnotti, John F.
Beauchamp, Michael S.
Using principal component analysis to characterize eye movement fixation patterns during face viewing
title Using principal component analysis to characterize eye movement fixation patterns during face viewing
title_full Using principal component analysis to characterize eye movement fixation patterns during face viewing
title_fullStr Using principal component analysis to characterize eye movement fixation patterns during face viewing
title_full_unstemmed Using principal component analysis to characterize eye movement fixation patterns during face viewing
title_short Using principal component analysis to characterize eye movement fixation patterns during face viewing
title_sort using principal component analysis to characterize eye movement fixation patterns during face viewing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6833982/
https://www.ncbi.nlm.nih.gov/pubmed/31689715
http://dx.doi.org/10.1167/19.13.2
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