Expression-dependent susceptibility to face distortions in processing of facial expressions of emotion.


Journal

Vision research
ISSN: 1878-5646
Titre abrégé: Vision Res
Pays: England
ID NLM: 0417402

Informations de publication

Date de publication:
04 2019
Historique:
received: 06 10 2017
revised: 02 02 2018
accepted: 04 02 2018
pubmed: 3 3 2018
medline: 7 1 2020
entrez: 3 3 2018
Statut: ppublish

Résumé

Our capability of recognizing facial expressions of emotion under different viewing conditions implies the existence of an invariant expression representation. As natural visual signals are often distorted and our perceptual strategy changes with external noise level, it is essential to understand how expression perception is susceptible to face distortion and whether the same facial cues are used to process high- and low-quality face images. We systematically manipulated face image resolution (experiment 1) and blur (experiment 2), and measured participants' expression categorization accuracy, perceived expression intensity and associated gaze patterns. Our analysis revealed a reasonable tolerance to face distortion in expression perception. Reducing image resolution up to 48 × 64 pixels or increasing image blur up to 15 cycles/image had little impact on expression assessment and associated gaze behaviour. Further distortion led to decreased expression categorization accuracy and intensity rating, increased reaction time and fixation duration, and stronger central fixation bias which was not driven by distortion-induced changes in local image saliency. Interestingly, the observed distortion effects were expression-dependent with less deterioration impact on happy and surprise expressions, suggesting this distortion-invariant facial expression perception might be achieved through the categorical model involving a non-linear configural combination of local facial features.

Identifiants

pubmed: 29496513
pii: S0042-6989(18)30019-1
doi: 10.1016/j.visres.2018.02.001
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

112-122

Informations de copyright

Copyright © 2018 Elsevier Ltd. All rights reserved.

Auteurs

Kun Guo (K)

School of Psychology, University of Lincoln, UK. Electronic address: kguo@lincoln.ac.uk.

Yoshi Soornack (Y)

School of Psychology, University of Lincoln, UK.

Rebecca Settle (R)

School of Psychology, University of Lincoln, UK.

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Classifications MeSH